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NCI Enterprise Vocabulary Services (EVS) provides resources and services to meet NCI needs for controlled terminology. NCI needs for terminology content, standards and systems are often shared by other NIH institutes and federal agencies, and by the broader cancer research and biomedical community. Since 1997, EVS has worked with many partners to build shared content and services, so that information can be exchanged, interpreted and analyzed while reducing total effort and cost. EVS is widely used:

  • By NCI, NIH, federal agencies, and other U.S. and international biomedical, academic, standards and research organizations
  • To create, extend, subset, map, access and publish biomedical terminology and ontology
  • For basic, translational, and clinical research, clinical care, epidemiology, public health, administration, and public information.

These pages describe many of the users and uses of EVS resources and services, almost all of which are freely available without restriction. Sections 2-10 give an overview of the channels, volume, and composition of use. Sections 11-14 profile key partners and users. The bibliography includes more than 400 scientific publications on the development and use of EVS.

1 - Executive Summary

EVS supports a broad community of users, growing from its core mission of meeting NCI terminology needs to also support community-driven standards for data exchange and interoperability in clinical trial, research and other activities. Over the last dozen years, this community has contributed to EVS content, technology, and dissemination, achieving results beyond what the individual participants could have achieved separately.

This document briefly outlines EVS resources; collects available statistics, for both NCI and related systems, to capture the level, composition, and nature of use; and then fleshes this out with operational details, user profiles and other information, including top users of specific resources. Each section focuses on a particular topic, with selected highlights gathered here.

User Profiles

  • EVS terminology content, standards and technology are adopted and actively used by many organizations and communities, some directly involving tens of thousands of users. Examples include the following:
    • Clinical trial programs such as CCOP, which uses NCIt to code the care of 85,000 patients.
    • TCGA content, used by at least 59 academic, commercial and non-profit organizations in 25 studies.
    • caNanoLab, still heavily used with more than 420,000 visitors since June 2011.
    • NIH use such as at NICHD, which employs NCIt pediatric terminology for the coding of 848 clinical trials, used by 52 different academic and research organizations.
  • Many of these standards extend beyond the NCI and NIH community, and are jointly developed with and adopted by outside agencies and standards organizations that shape NCI's research environment.  Examples include the following:
    • NCIt content has become a regulatory standard through shared use by FDA, CDISC and others, such as for drug submissions and Therapeutic Area Standards.
    • FDA reported receiving 14,014 electronic submissions using NCIt-based regulatory coding between Oct. 2009 and September 2010; such submissions have likely grown substantially since.
    • NCPDP use of NCI's drug standard terminology extends to some 200 vendors serving approximately 15,000 pharmacies with more than 4 billion annual transactions nationwide, including large providers such as First DataBank and Surescripts, the nation's largest e-prescriber.
    • Surescripts alone connects thousands of pharmacies across the US, and is connected to the largest network of payers and Medicaid Fee for Service payers nationwide.
  • Numerous academic, research, and even commercial organizations are also using and extending the capabilities of LexEVS and other EVS tools, for use throughout their organizations and for the development of commercial products.

Shared Terminology Development

  • Over the last 10 years, NCIt has been adopted by FDA, CDISC, NCPDP and other partners as a shared framework for developing terminology standards, allowing participants to compare and harmonize with each other's content while taking advantage of full-text definitions, codes, and other features.
  • About 40% of the 120,000 current NCIt concepts have terms from both EVS and from one or more of these other sources.

Use of EVS Servers

  • Monthly ftp downloads of EVS terminology files have grown from 75,000 monthly in the first half of 2014 to more than 100,000 monthly in 2015 and 2016, not including downloads from CDISC, FDA, and other non-EVS servers.
  • EVS browsers, using LexEVS local APIs, are used each month by about 3,500 unique visitors making more than 6,000 visits.
  • Top users include NCI (CBIIT and others), various parts of NIH, FDA, many universities and biomedical companies, and other biomedical organizations.
  • LexEVS distributed (remote) APIs are used by an average of 60 unique "visitors" making some 500 visits monthly; these are mostly applications, including many at NCI and NIH as well as at universities and biomedical research organizations.
  • The Term Suggestion site has about 110 unique visitors with 180 visits monthly, often suggesting changes or additions involving multiple terms.

Use of EVS Content on Select Non-EVS Servers

  • Over 30,000 unique visitors account for some 70,000 page views monthly on the NCI Drug Dictionary, built from NCI Thesaurus drug content and representing some 2-3% of all Cancer.gov use.
  • FDA-hosted files and mail lists are used by tens of thousands of users for EVS supported FDA terminology standards, used on FDA internal servers for coding and validation of tens of thousands of data submissions each month.
    • The SPL mail list alone has some 40,000 subscribers, and 9,466 different entities in more than 100 countries use this terminology to list their products.
    • CDRH alone reports 35,000 safety report submissions a month based on NCI Thesaurus terminology.
  • CDISC server registered downloads 2009-2011 included 2,973 users from 1,524 organizations for SDTM, and 866 users from 603 organizations for CDASH.
    • Combined with downloads from NCI servers, total downloads of CDISC standard terminology files exceeded 15,000 from more than 90 countries, primarily for institutional use.
  • NLM redistributes NCI Thesaurus to its 5,500 UMLS users, and gets 10,000,000 hits monthly on its 27,000 DailyMed SPL files all using EVS maintained SPL terminology.

Use of EVS Tools

  • EVS software is available as open source for community code contributions and reuse.
  • EVS has supported Mayo Clinic development of the LexEVS terminology servers, now being deployed at MD Anderson, Stanford, Emory, Ohio State University Medical Center, Georgetown University, Washington University, and National Cancer Research Institute (NCRI)/UK CancerGrid, as well as IBM and GE Healthcare.
  • Several of these sites are also adopting EVS browser software.
  • EVS has worked with Stanford and others on NCI Protégé and related tools.
    • NPO is the only direct adopter of the NCI-customized versions of Protégé, but there has been extensive sharing of underlying Protégé and other development work.
  • Other tools in development are also expected to have significant outside adoption.


The primary EVS website is an informational site with links to the other EVS tools and browsers and to multiple downloadable files. It has seen increasing use over time, now averaging over 6000 visitors and over 11,000 visits per month

 A line graph indicating the increase in visitors from 2013 to 2016.

Bibliography on EVS

  • The EVS bibliography includes more than 400 scientific journal articles and related literature, covering
    • Overviews and analyses of EVS.
    • Programs and projects that discuss explicitly their use of EVS resources in support of cancer research and other scientific efforts.

2 - EVS Resources

This section outlines the EVS terminology content and tools whose use is described in the subsequent sections of the EVS Use and Collaborations document. The following resources are described.

Associated downloads are being made available on the EVS Downloads web page.

Controlled Terminology

EVS produces two major reference resources, NCI Thesaurus and NCI Metathesaurus, in collaboration with a range of partners. EVS also produces, licenses, processes and makes available a wide range of other terminology content.

NCI Thesaurus (NCIt)

NCIt is NCI's reference terminology and core biomedical ontology. It covers some 120,000 key biomedical concepts with a rich set of terms, codes, 115,000 textual definitions, and over 400,000 inter-concept relationships, and is used to code most NCI metadata and models. More than 500 concepts are added each month, and many more existing concepts are updated, in response to user requests and the requirements of dependent systems and applications. Many of these concepts include content created and maintained jointly with NCI's partners, making NCIt a shared coding and semantic infrastructure resource (see Shared Terminology Development section).

NCI Metathesaurus (NCIm)

NCIm currently consists of more than 85 biomedical terminologies whose 6,700,000 terms are mapped to 2,800,000 concepts representing their shared meanings; there are more than 31,000,000 cross-links between content elements. NCIm is updated approximately six times a year, growing by some 100,000 concepts annually. This growth involves adding new terminologies, and updated versions of existing terminologies, to meet the requirements of EVS users for specific terminologies and for mappings between them. NCIm also provides a rich reference resource for a broad range of users seeking definitions, synonyms, codes, and other information.

Other Terminologies

EVS licenses, processes and makes available many other terminologies through standardized application and browser interfaces, and frequently through various data file formats as well. EVS has helped create, harmonize with, and publish several of these terminologies. Currently available are:

  • ChEBI: Chemical Entities of Biological Interest
  • CTCAE: Common Terminology Criteria for Adverse Events
  • GO: Gene Ontology
  • HGNC: Human Genome Organisation (HUGO) Gene Nomenclature Committee
  • HL7: Health Level 7 RIM V3
  • ICD-9-CM: International Classification of Diseases, Ninth Revision, Clinical Modification
  • ICD-10: International Classification of Diseases, Tenth Revision
  • ICD-10-CM: International Classification of Diseases, Tenth Revision, Clinical Modification
  • LOINC: Logical Observation Identifiers Names and Codes
  • MA: Adult Mouse Anatomy
  • MedDRA: Medical Dictionary for Regulatory Activities Terminology
  • MGED Ontology: Microarray Gene Expression Data Ontology
  • NDF-RT: National Drug File Reference Terminology Public Inferred Edition
  • NPO: NanoParticle Ontology
  • OBI: Ontology for Biomedical Investigations
  • PDQ: Physician Data Query
  • RadLex: Radiology Lexicon
  • SNOMED CT: Systematized Nomenclature of Medicine-Clinical Terms
  • UMLS SemNet: UMLS Semantic Network
  • Zebrafish: Zebrafish Model Organism Database

Terminology Value Sets

Value sets provide a standardized representation of selected values from these terminologies, following the Common Terminology Services Release 2 (CTS 2) specification. More than 700 value sets are currently defined in the EVS LexEVS 6 server, covering a range of standards from CDISC, FDA, NCPDP, NDF-RT, NICHD, and others.

Terminology Mappings

Mappings between several supported terminologies have also been published:

  • GO to NCIt Mapping
  • Mouse Anatomy (MA) to NCIt Mapping
  • NCIt to ChEBI Mapping
  • NCIt to HGNC Mapping
  • NCIt to SwissProt Mapping
  • PDQ to NCIt Mapping
  • ICD-O-3.1 to NCIt Mapping

EVS Terminology Tools

LexEVS

LexEVS is a set of software and services to load, publish, and access vocabulary and ontology resources.  EVS has supported and migrated to LexEVS, developed by the Mayo Clinic, as an open source tool that is freely sharable and that is now being deployed at a number of other partner organizations such as MD Anderson, Stanford, Emory, Ohio State University Medical Center, Georgetown University, Washington University, and National Cancer Research Institute (NCRI)/UK CancerGrid, as well as by commercial vendors such as IBM and GE Healthcare.

EVS Terminology Browsers

EVS has developed two cross-linked, user-friendly terminology browsers: NCI Term Browser and NCI Metathesaurus Browser. These browsers are designed to meet NCI's internal and public information needs across the full range of EVS content, and also provide important support for collaborative content and standards development. EVS browsers are heavily used from both NCI servers and non-NCI adopter sites. The browsers are freely available as open source software.

NCI Protégé

EVS editing software has been based on Stanford's open source Protégé tool, widely used for editing biomedical terminology and ontologies. NCI extensively customized Protégé to meet EVS requirements and business rules, contributing its code back to the community to help support Protégé development. The most demanding use is as the editing software for NCIt, but Protégé is also used locally for CTCAE and other editing work, and by NanoParticle Ontology (NPO) (see Nanotechnology user profile).

EVS Value Set Editor

The Value Set Editor is used to create and maintain CTS 2 value set and pick list definitions for loading into the LexEVS server, which resolves the definitions against referenced terminologies. The Editor was initially developed to support internal EVS operational requirements, and is used to generate the value sets currently published by EVS (see NCI Term Browser). Source code is available for community contributions and reuse.

EVS Mapping Tool

The Mapping Tool supports mapping between term lists, value sets, terminology subsets or whole terminologies. EVS has long had frequent requests and operational requirements for such mappings, and has done extensive requirements gathering. EVS has created an initial release to support internal operations; it provides a mix of automated and manual features for creating, editing and publishing mappings, connecting to LexEVS for terminologies available there and producing XML mapping representations that can be loaded into and accessed through LexEVS.

Term Suggestion

EVS Term Suggestion software is used extensively – both standalone and integrated into the EVS terminology browsers – to get community feedback and contributions to both NCI and EVS partner terminology products.

EVS Report Writer

EVS Report Writer is standalone software that connects to the LexEVS servers. It is used internally to generate many value sets and other reports, and is available for use by others.

LexWiki and BiomedGT Semantic Media Wiki

The BiomedGT wiki was initially developed by Apelon as a platform for collaborative vocabulary development by EVS and its partners. Independently, the Mayo Clinic was developing LexWiki, a similar tool based on Media Wiki. Interactions and cross-fertilization of ideas between the two groups led to a number of enhancements in the production version of the BiomedGT wiki used by EVS, as well in LexWiki in use at Mayo. Both LexWiki and BiomedGT wiki are published as open source by the Mayo Clinic, through the Vocabulary Knowledge Center. BiomedGT Semantic Media Wiki has been used for NCI terminology such as CTCAE and BiomedGT, as well as partner efforts such as the NanoParticle Ontology (NPO) based at Washington University, but has not won broader adoption.

3 - Use of Browsers and LexEVS Local Java API on EVS Servers

The next several sections analyze available statistics for direct use of EVS resources on EVS servers. This section covers direct use of NCI-hosted EVS terminology browsers and the LexEVS local java API that supports them. Later sections cover the LexEVS distributed (remote), LexEVS caGrid services, the term suggestion site, and ftp download services.

The EVS browsers and LexEVS APIs at NCI are covered by the AWStats and Wusage applications, recording various statistics including number of unique visitors, visits, number of pages viewed and data volume accessed. These have been collected below to give a window into the use of the EVS services. Appendix 1 lists the usage tracking pages from which these statistics are gathered.

We are now able to identify and exclude most web crawler and similar automated hits, as well as internal use for testing and development purposes. Detailed examination of addresses and patterns of use suggest that the use reported below overwhelmingly represents real users from NCI and other organizations of interest. Analyzed below are figures for unique visitors and number of visits, as well as the volume of data communicated, which for all but ftp sites reflects high volumes of traffic in mostly compact units of terminology concept data.

Statistical Overview

The EVS browsers run off the LexEVS local API, and provide the highest number of direct users of EVS terminology servers. The lowest, but most informative, numbers are for unique visitors and number of visits. The number of unique visitors to the EVS family of browsers has averaged around 4,000 per month in 2015, growing to around 10,000 per month by the end of 2016 generating some 17,000 visits.

Unique visitors to EVS browsers, per month
Chart showing unique visitors to EVS browsers per month

Key

Abbreviation

Site

NCITerms

NCI Term Browser (many terminologies, value sets and mappings)

NCIt

NCI Thesaurus Browser (now one face of NCITerms, counted separately)

NCIm

NCI Metathesaurus Browser (separate app, searchable from NCITerms)

TermForm

EVS Term Suggestion site (covered later)

 

Number of visits to EVS browsers, per month 
Chart showing number of visits to EVS browsers per month

Browser Users

The data below is from January - December 2015. It shows an overall view of which internet domains hit EVS browsers most heavily, and then breaks down each domain by top users. All browsers are put together for this analysis.

Overall Use

The amount of bandwidth used was divided into four (4) major groups: Government .gov users, private .com and .net users, Educational .edu institutions, and Organizations .org. The data was analyzed and bots were discarded to avoid skewing the data.

Data accessed (MB) from EVS browsers by domain

Domain

MB

org

256031

com

201510

gov

30318

edu

30293

Data accessed (MB) from EVS browsers by domain
Chart showing data access(MB) from EVS browsers by domain

Users from .gov domain

The numbers below show the top government users of the EVS browsers, as measured by the MB of data that were accessed. Agency names have been provided where they are known. The main users are internal to NCI and NIH, which is not surprising, but we have substantial FDA usage as well.

Top .gov EVS browser users: Data accessed (MB)

Organization

MB

National Institutes of Health16102
NIH-NCI9992
FDA3518
NIH-NIDA220
National Library of Medicine104
U.S. Dept. of Health and Human Services91
Illinois Century Network28
NIH-NHLBI24
Department of Homeland Securty21
State of Alabama, Information Services Division12

 

Top .gov EVS browser users: Data accessed (MB)
chart showing data access by dot gov EVS browser users

Users from .com domain

We combined the .net and .com users into one group as both contained a number of private corporations and individual users from a wide variety of internet service providers (ISPs). Business names have been provided where known. The graph below does not show ISPs as they are not interpretable and dwarf the next largest groups.

Top .com EVS browser users: Data accessed (MB)

Business

MB

Microsoft Corporation49478
LEO Pharma

15236

Pharmaceutical Product Development, Inc.13037
Bayer8735
Pfizer Inc.7918
Johnson & Johnson7431
Boehringer Ingelheim Pharmaceuticals, Inc.6954
Merck and Co., Inc5078
Level 3 Communications, Inc4842
Glaxo SmithKline4819

Top .com EVS browser users: Data accessed (MB)
chart showing data access by dot com EVS browser users

Users from .edu domain

The edu users were widely distributed across many American universities and a small selection of foreign institutions.

Top .edu EVS browser users: Data accessed (MB)

Institution

MB

Mayo Clinic 11885
MD Anderson Cancer Center - University of Texas 2865
Ohio State University Medical Center 2743
University of Utah 1694
University of Colorado Hospital 1122
Johns Hopkins Medicine 1038
Washington University 891
University of Pittsburgh Medical Center 842
University of Michigan Health System688
Vanderbilt University684

Top .edu EVS browser users: Data accessed (MB)
chart showing data access by dot edu EVS browser users

Users from .org domain

Like the .edu users, the .org users were widely distributed across a variety of organizations.

Top .org EVS browser users: Data accessed (MB)

Organization

MB

Sloan-Kettering4498
H. Lee Moffitt Cancer Center1464
Parners HealthCare1138
Roswell Park Cancer Institute 799
Providence Health & Services799
Assistance Publique-Hopitaux de Paris786
St. Jude Childrens Research Hospital471
Northwestern Memorial Hospital443
Massachusetts General Hospital 413
Aurora Health Care 408

Top .org EVS browser users: Data accessed (MB)
chart showing data access by dot org EVS browser users

Usage Patterns of the Three EVS Browsers

The three browsers offered by EVS are

NCITermsNCI Term Browser: Access to all terminologies, value sets and mappings.
NCItNCI Thesaurus Browser: Now just one face of NCITerms, but the most used and counted separately.
NCImNCI Metathesaurus Browser: A separate application, searchable from NCITerms.

broswer usage pie chart

Nearly 75% of all use goes to the NCIt browser, including almost all use from the educational domain and two thirds of government use.  By contrast, half of commercial domain use goes to NCI Terms, while .org use is almost evenly split between all three browsers.

dot com browser usage pie chart  dot edu browser usage pie chart dot gov browser usage pie chart dot org browser usage pie chart

4 - Use of LexEVS Java API on EVS Servers

The next sections cover the following topics.

Statistical Overview

The LexEVS Distributed (Remote) Java API has averaged 70 "visitors" a month for all production versions combined, generating 500 visits monthly with an average length of over 30 minutes. Applications that latch onto the API and maintain contact will count as a single visit, accounting for the substantial number of long visits.

In 2014 EVS introduced the LexEVS CTS2 service - a REST interface into the EVS data.  This service saw growing use over the last three years.

The 5.1 API was deprecated and phased out after October 2015.  LexEVS 6 APIs all now point to the most recent release, and users are encouraged to use the more generic LexEVS6 addresses.


Unique visitors to LexEVS Java APIs per month

Unique visitors Jan 2012 - Dec 2017. LexEVS 5.x and 6.x declining. LexEVS CTS2 started 2014.

Number of visits to LexEVS Java APIs per month
Number of visits Jan 2012 - Dec 2017. LexEVS 5.x and 6.x declining. LexEVS CTS2 started 2014.

Duration of visits to LexEVS Java APIs
Chart showing duration of visits to LexEVS Java APIs. Refer to table after image for raw data.

The table below shows the raw data on visit duration used to generate the pie chart above. The statistics program recorded how long each visitor spent connected to an API and dropped it into one of 8 time categories. The number in each category for each API was then totaled and displayed as a percentage pie chart.

Number of visits to the LexEVS APIs by duration (used in pie chart above)

Duration

LexEVS 4.2 and 5.0

LexEVS 5.1

LexEVS 6.0

Total

0s-30s

116
159
125
400

30s-2mn

205
283
216
704

2mn-5mn

9
36
101
146

5mn-15mn

33
44
33
110

15mn-30mn

149
180
196
525

30mn-1h

224
339
263
826

1h+

231
674
340
1245

Unknown

1
3
1
5

Below is a chart of the bandwidth of data pulled from the various APIs for the past year, measured in gigabytes. The 5.1 API is deprecated and its usage is low.  Some CBIIT projects being released in Fall 2015 draw heavily on the APIs, further increased as they went through QA, but activity continues well above the previous baseline seen before June 2015 as these new projects take advantage of the LexEVS 6.x capabilities.

Data downloaded (GB) via LexEVS APIs per month

Data downloaded Jan 2012 - Dec 2017.

LexEVS Java API Users

Use of the LexEVS Java API requires programming knowledge and an effort to understand the LexEVS model. This results in a much smaller user base than for the EVS browsers, but these users are often important NCI, NIH, and external applications that in turn reach a much larger user base. Data below cover the months from January - December 2015, and show that the primary users of the API are within the .gov domain. The .edu,.com/.net and .org users are mostly a small subset of top users of other interfaces, but are too sparse to make separate breakdowns useful.

Data accessed (MB) from LexEVS distributed Java APIs by domain

Domain

MB

gov

1156

com/net

753

edu

380

org

0

Data accessed (MB) from LexEVS distributed Java APIs by domain
Chart showing data accessed (MB) from LexEVS distributed Java APIs by domain. Refer to table before image for raw data.

Users from .gov domain

The highest-volume users are within NCI. The next largest group of users are those within NIH for whom more specific affiliation could not be determined.

Top .gov LexEVS distributed API users: Data accessed (MB)

Entity

MB

National Institutes of Health 1143
DEPARTMENT OF HOMELAND SECURITY 11
National Cancer Institute 0.6
National Library of Medicine 0.6
U.S. Dept. of Health and Human Services 0.4

Top .gov LexEVS distributed API users: Data accessed (MB)
Chart showing top government LexEVS distributed API users. Refer to table before image for raw data.

5 - Use of LexEVS caGrid Services on EVS Servers

The next sections describe the following services, which are in the process of being retired:

In addition to the Java API, EVS made available LexEVS Data and Analytical Services on caGrid.  These services are close to final retirement and being shut down, so this section is mostly of historical interest.

LexEVS caGrid Data Services

LexEVS Data Services on caGrid allow users to use CQL queries to obtain SOAP-based LexGrid XML results.

Unique visitors to LexEVS caGrid Data Services, per month
Chart showing unique visitors to LexEVS caGrid Data Services per month

Number of visits to LexEVS caGrid Data Services, per month
Chart showing number of visits to LexEVS caGrid Data Services per month

 

LexEVS caGrid Analytical Services

EVS also makes available LexEVS Analytical Services through caGrid, supporting method calls with a variety of parameters and returned LexEVS objects. This shows a similar pattern to the data services, with the retirement of 5.0 showing a drop in activity on the graph, but the 5.1 and 6.0 services remain steady.

Unique visitors to LexEVS caGrid Analytical Services, per month
Chart showing unique visitors to LexEVS caGrid Analytical Services per month

Number of visits to LexEVS caGrid Analytical Services, per month
Chart showing number of visits to LexEVS caGrid Analytical Services per month

 

6 - Use of Term Suggestion Site on EVS Servers

EVS makes available a web site for users to suggest new terms or changes to existing terms in the NCI Thesaurus, NCI Metathesaurus, and other terminologies partnered with EVS such as CTCAE and NPO. In 2016 the site averaged about 250 visits by 120 unique visitors each month; a single visit/submission sometimes covers multiple terms (e.g., a single submission requested 28 new ECG test result values for CDISC). Not all visits result in submissions.

Unique visitors to Term Suggestion site, per month
Chart showing unique visitors to Term Suggestion site per month

Number of visits to Term Suggestion site, per month
Chart showing number of visits to Term Suggestion site per month

CDISC is one of the busiest users of the Term Suggestion application. The nature of term suggestions means this application usage will always be volatile. A CDISC meeting or spreadsheet can generate a large number of terms one month and drop off substantially the next.

Number of suggestions to CDISC concepts in NCIt, per month
CDISC Suggestions chart

 

7 - Use of FTP Services on EVS Servers

Downloads of EVS terminology files have grown to an average of over 100,000 each month.

Number of files downloaded January 2013 - September 2015

 

Downloads Through HTTP Access to EVS FTP Site

EVS provides an HTTP interface to the FTP site allowing users to download files directly from the web, without the need for using an FTP client, at http://evs.nci.nih.gov/ftp1/.

In June 2014 EVS began publishing disjoint branches of the NCI Thesaurus, allowing NCIt users to select their domain of interest rather than downloading the entire vocabulary.

Number of ftp files downloaded via HTTP, by top-level folder, for January 2014 - December 2017

 

 

 

Top Level FolderJan'14FebMarAprMayJuneJulyAugSepOctNovDecJan '15FebMarAprMayJuneJulyAugSepOctNovDecJan '16FebMarAprMayJuneJulyAugSepOctNovDec

Jan '17

FebMarAprMayJuneJulyAugSepOctNovDec
CDISC18,73317,398 19,92321,33018,751 20,471 24,088 22,000 22,16624,11919,70520,53924,46127,88333,11529,08225,18829,11430,66430,04527,70430,90928,31826,753281133887130909277872750328425262731892514897239042728023896262132701729390322592597532995283672912035147262132823627327
CTCAE31,91131,820 33,31124,116 32,083 32,643 36,567 35,749 37,09739,732

34,165

33,28037,57038,09643,42738,54639,68740,35041,02039,26641,31947,76146,12544,057473005075255204549815781859425541863771733905599678160672433781886451780701751359283995123925801035621063357818910960696003
FDA8,9109,331 8,1506,537 7,256 8,205 12,127 9,282 10,0347,4036,8037,4868,74819,55214,81612,03510,0948,67211,43817,1039,70210,6098,44412,52312147185389332105609329925188769010649569976903932380116523896095848173110968399852613615801155796893
FMT19621823023718217224923126522713817529649148227215517127835315923416120731038119716626113311449078115121147164112163170109139124106123164152164
Metathesaurus499359 317159 291 287 464 558 6405613813487101,1141,174519236304491849473414372382705668453478545427402219217235200260250266264308290297319299282250133230
NCPDP4,1674,9615,5835,9165,5736,7787,5916,4835,7325,7335,2495,6105,9106,8856,7016,1838,4336,1296,6187,2206,4946,4715,7476,065606368577208877880216964615341333460239928662806263924013300356125592996268825202715263924422217
NDF-RT1,6132,556 2,4262,095 1,976 2,848 3,378 2,240 2,7892,3912,3023,1814,2015,3854,1983,1623,7676,6485,3356,3673,6143,8783,6584,0214272610760939072849073768333423802244235829483086301929562927225423133855363433824291301924643623
NICHD1,8001,652 1,8651,533 1,341 1,017 1,400 1,094 2,0641,1579251,7692,0454,2774,0352,6622,4862,0803,6277,4804,6344,1653,7296,669549678364472419854724875347926183909405131093625288925164368457645743707394245977719288924982099
Thesaurus5,8215,515 4,7052,724 4,821 7,277 9,535 7,064 8,2347,0047,9366,75913,17724,14825,02711,9105,4504,8468,57219,6428,6068,9258,4089,882142781679271129732877276157277363848981084786508839538164527515805253777404104801330111627538152778920
Branches     2372471551001528898182260302180131128191283255206192156257242484282753583425263229305352191114101205185101240187193248114142171
Totals73,65073,81076,510 64,647 72,274 79,93595,64684,85689,12188,47977,70279,24597,300128,091133,155104,37195,49698,442108,234126,608102,960113,572105,154110,7451189411470441214642076901233811214661115497899370332111178134035124606126868112861137793136084142310157942150720165606182102126868156529147647

Downloads Through FTP site directly

Some users download files using an FTP client directly from the FTP site at ftp://ftp1.nci.nih.gov/pub/cacore/EVS/.  These numbers are recorded separately and are presented below.

Number of ftp files downloaded via FTP, by top-level folder, for Jan 2014 - December 2017

 

 

Top Level FolderJan'14FebMar Apr May June July Aug SepOctNovDecJan'15FebMarAprMayJuneJulyAugSepOctNovDecJan '16FebMarAprMayJuneJulyAugSepOctNovDecJan '17FebMarAprMayJuneJulyAugSepOctNovDec
CDISC1420 745413 20 30 851936323244253034443325371914148143411540823109211733924197272294101867414402
CTCAE787378 79 43 40 40 30 195247320729464843462833204320222765388331383964801414101117100
FDA646652351 553 676 750 739 198 73356078843611636829322929330973882025227512819421632533288222255088518520716110540531948818223115745163932
FMT                                    100000000010
Metathesaurus38514 169124031222122700274300003007000020000000
NCPDP7120213005233400500233201621126601210012228067000000600
NDF-RT70103203169 108 93 97 195 2031032021882911019178252339410

303

278384136256423386457914138831821210821112031034235272716664825172210
NICHD4117 11 80095000221014121171313126141012260397031419181126112001
Thesaurus81119131 58 38 102 118 62 129847868293444421927151462367241248711360212230331874130532602242353388108012852
Branches     13102722700080332400125232116113004000100000000000
CareLex                                    022000000000
GAIA                                    022100000000

 

 

 

 

8 - Use of EVS Content on Select Non-EVS Servers

Much use of EVS content comes from redistribution on non-CBIIT Servers. Here are a few of the major redistribution points for which meaningful use information is available.

Cancer.gov Servers

The NCI Office of Communications and Public Liaison (OCPL) has been a partner in, contributor to, and user of EVS since 1999 (see NCI User Profile section). The NCI Drug Dictionary is a joint effort providing NCI Thesaurus drug definitions and other information linked to active and closed cancer clinical trials, and is used by tens of thousands of visitors monthly representing some 2-3% of the Cancer.gov total.

Cancer.gov NCI Drug Dictionary use, monthly average (January 2010 - June 2011)

Average Monthly

NCI Drug Dictionary

Cancer.gov Global

% of Cancer.gov

Unique Visitors

37,807

1,336,758

2.8%

Visits

41,784

1,570,970

2.7%

Page Views

76,417

4,530,721

1.7%

Time Spent

4.98 minutes

7.6 minutes

 

OCPL also disseminates EVS content in a variety of other ways, including the Cancer.gov Terminology Resources pages.

FDA Servers

FDA publishes many NCIt-based FDA terminology code lists on its own servers, while also pointing to EVS servers for file, API and browser access. FDA also mounts these and EVS-maintained CDISC lists on internal servers for extensive data submission validation and other purposes.

CDISC Servers

CDISC STDM, CDASH and several other vocabulary subsets are maintained in the NCI Thesaurus. EVS personnel generate monthly files which are posted to the CBIIT FTP site and also redistributed by CDISC. Downloads registered by CDISC from their own site 2009-2011 yield the following overview counts.

  • SDTM – 2,973 registered downloads from individuals in 1,524 companies/organizations
  • CDASH – 866 registered downloads from individuals in 603 companies/organizations

The figures below show registered downloads by top downloaders of this data from CDISC servers, aggregated by company or organization. The individuals and contact information for these downloads are known to CDISC.

Top downloaders of SDTM files from CDISC servers, by number of downloads (2009-2011)
chart showing top downloaders of SDTM files from CDISC servers by number of downloads

Top downloaders of CDASH files from CDISC servers, by number of downloads (2009-2011)
Chart showing top downloaders of CDASH files from CDISC servers by number of downloads

NLM

NLM's UMLS, with some 5,500 registered users, is an important user and redistributor of NCI Thesaurus and other EVS content. While no statistics are available, NCIt is one of the larger and most content-rich sources within UMLS, and UMLS file and browser redistribution of NCI content is a significant source of use.

NLM gets over 10,000,000 hits monthly on its 27,000 DailyMed SPL files, all of which are coded using EVS maintained SPL terminology from NCI Thesaurus.

VKC

The Vocabulary Knowledge Center had 19,156 unique visitors with 89,649 page views from June 1 2010 – June 30 2011. This includes a significant quantity of EVS resources, but breakdowns specific to EVS are not currently available. (Note: VKC Services were suspended May 12, 2012. The Vocabulary Knowledge Center wiki and forums remain available for browsing.)

NCBO BioPortal

NCBO BioPortal is an important access and distribution point for NCI Thesaurus, with an average of 1,000 to 2,000 NCIt visits each month and about 20 projects registered there as NCIt users.

9 - Use of EVS Tools

EVS services depend on the full set of EVS tools: EVS requires editing, loading, serving, browsing, reporting and related software tools to exist in its current form. Wherever feasible, EVS has sought to use and contribute to tools created and used by others in the community, and EVS ensures that virtually all of its software is available as open source for community code contributions and reuse (the significant exception is NLM-controlled software used for maintenance of NCI Metathesaurus). All released EVS tools have direct users beyond basic EVS operations, and all development efforts are also expected to have at least some external adoption at other sites.

The sections below outline such use beyond EVS operations, without trying to duplicate more detailed coverage in the use statistics and user profile sections of this report.

LexEVS Servers and Loaders

LexEVS is an openly available terminology server and set of services developed with the Mayo Clinic. Extensive external use of EVS server services is documented elsewhere. This software is now being deployed at a number of other partner organizations such as MD Anderson, Stanford, Emory, Ohio State University Medical Center, Georgetown University, Washington University, and National Cancer Research Institute (NCRI)/UK CancerGrid, as well as by commercial vendors such as IBM and GE Healthcare. Source code is available on GitHub (see LexEVS GitHub repository Exit Disclaimer logo ) for community contributions and reuse.

NCI Terminology Browsers

EVS terminology browsers have been designed and developed at NCI to provide more powerful and user-friendly access than that provided by earlier propriety software. Extensive external use of EVS browsers hosted locally is documented elsewhere. Several sites adopting the LexEVS server are also adopting EVS browser software (see User Profile sections). Source code is available on GitHub (see the NCI Term Browser GitHub repository Exit Disclaimer logo and NCI Metathesaurus Browser GitHub repository) Exit Disclaimer logo for community contributions and reuse.

NCI Protégé

EVS has worked over several years with Stanford and others on Protégé and related tools. Protégé editing software has been extensively customized for editing large production biomedical terminologies and ontologies. NCI customizations have long been available for download, but these download have not been tracked. We do know that NCI Protégé is used by NanoParticle Ontology (NPO) (see Nanotechnology user profile).

Wikis

BiomedGT Semantic Media Wiki collaborative terminology development environment has been used for NCI terminology such as CTCAE and BiomedGT, as well as partner efforts such as the NanoParticle Ontology (NPO) based at Washington University, but has not won broader adoption.

LexWiki was created by the Mayo Clinic, developed further for the BiomedGT wiki, then updated and published as the open source LexWiki tool by the Mayo Clinic, through the Vocabulary Knowledge Center.

Other Tools

EVS Report Writer and Term Suggestion software are used extensively to support NCI work as well as external partners such as FDA, CDISC, and NCPDP. Source code is available on GitHub (see NCI Report Writer GitHub repository Exit Disclaimer logo and NCI Term Suggestion GitHub repository Exit Disclaimer logo ) for community contributions and reuse, but we have no reports of adoption at other sites.

NCI Value Set Editor, for creating and maintaining CTS 2 value sets and pick lists, was initially developed to support internal EVS operational requirements, and is used to help generate the value sets currently published by EVS (see NCI Term Browser). Source code is available on GitHub (see the NCI Value Set Editor GitHub repository Exit Disclaimer logo ) for community contributions and reuse.

The NCI Mapping Tool has now reached the stage of initial prototype deployment for internal EVS use. EVS and the MedDRA Maintenance and Support Services Organization (MSSO) Exit Disclaimer logo are working with the UK Medicines and Healthcare products Regulatory Agency (MHRA) Exit Disclaimer logo on use of the EVS Mapping Tool to develop an initial proof-of-concept mapping from SNOMED CT to MedDRA, focusing on high frequency adverse event terms to test the possibility of automated conversion of EHR data to support more proactive identification of potential signals with drugs that have gained marketing approval. Source code is available on GitHub (see the NCI Mapping Tool GitHub repository) Exit Disclaimer logo or community contributions and reuse.

10 - Shared Terminology Development

Since 1997, EVS has worked with many partners to build shared content and services, so that information can be effectively exchanged, interpreted and analyzed while minimizing overall effort and cost. Much of this content has been created and published using NCI Thesaurus and NCI Metathesaurus, as outlined in the following sections and described in more detail in the individual user profiles later, where other shared development efforts are also described.

NCI Thesaurus (NCIt)

NCI Thesaurus (NCIt) is NCI's core reference terminology. Over the last 10 years, it has also been adopted by FDA, CDISC, NCPDP and other partners as a shared standards development and coding environment, allowing participants to compare and harmonize with each other's content while taking advantage of full-text definitions, codes, and other features.

About 50,000 out of the 120,000 current NCIt concepts have terms from both EVS and from one or more of these other sources. The chart below gives a detailed breakdown of the main areas of overlapping tagged content. Note that some partners have used NCI terms rather than tagging their own, so these figures understate the true extent of cross-source sharing.

NCIt concepts with terms from tagged outside sources (11.09d: September 2011)
chart showing NCIt concepts with terms from tagged outside sources

NCIt concepts with tagged synonyms from each outside source (13.03d: March 2013)

NCIt Concepts

Source

335

BIOCARTA

1,149

BRIDG

27

CADSR

921

CDC

9,003

CDISC

425

CRCH

6,368

CTCAE

4,421

CTRM

903

DCP

114

DICOM

695

DTP

16,334

FDA

126

HL7

215

ICH

156

JAX

255

KEGG

4

NCICB

5,154

NCI-GLOSS

517

NCPDP

1731

NICHD

169

PID

311

RENI

62

SEER

288

UCUM

25

ZFin

NCI Metathesaurus (NCIm)

NCI Metathesaurus (NCIm) was started in the late 1990s to gather up, translate between, and publish the many terminologies used by NCI, including some created in part or whole within NCI. Development of NCI Thesaurus started within NCIm as NewPDQ, an effort to extend and restructure the PDQ Terminology long used to code cancer clinical trial, research and public information resources. NCIm continues to provide a vital environment for EVS collaboration with NCI and other partners to develop, map and publish terminologies of shared interest, and responds to the requirements of a broad range of stakeholders within the cancer research and biomedical community.

NCIt tagged content from outside sources is imported into NCIm with separate source tags. NCIt-derived sources are grouped separately below. The NCIm tags are mostly identical or very similar to those in NCIt; exceptions are CADSR, converted to NCI; HL7, converted to NCI-HL7; and CTRM, which is not imported.

NCIm sources and the number of concepts to which each contributes (201105: May 2011)

NCIm concepts

Source Label - NCIt-derived

Source Name

334

BioC

BioCarta online maps of molecular pathways, adapted for NCI use, 1105E

1,139

BRIDG

Biomedical Research Integrated Domain Group Model, 1105E

921

CDC

U.S. Centers for Disease Control and Prevention, 1105E

5,636

CDISC

Clinical Data Interchange Standards Consortium, 1105E

406

CRCH

Cancer Research Center of Hawaii Nutrition Terminology, 1105E

6,368

CTCAE

Common Terminology Criteria for Adverse Events, 1105E

903

DCP

NCI Division of Cancer Prevention Program, 1105E

114

DICOM

Digital Imaging Communications in Medicine, 1105E

693

DTP

NCI Developmental Therapeutics Program, 1105E

15,471

FDA

U.S. Food and Drug Administration, 1105E

215

ICH

International Conference on Harmonization, 1105E

156

JAX

Jackson Laboratories Mouse Terminology, adapted for NCI use, 1105E

244

KEGG

KEGG Pathway Database, 1105E

85,314

NCI

National Cancer Institute Thesaurus, 2011_05E

5,084

NCI-GLOSS

NCI Dictionary of Cancer Terms, 1105E

125

NCI-HL7

NCI Health Level 7, 1105E

514

NCPDP

National Council for Prescription Drug Programs, 1105E

715

NICHD

National Institute of Child Health and Human Development, 1105E

169

PID

National Cancer Institute Nature Pathway Interaction Database, 1105E

310

RENI

Registry Nomenclature Information System, 1105E

287

UCUM

Unified Code for Units of Measure, 1105E

25

ZFIN

Zebrafish Model Organism Database, 1105E

NCIm concepts

Source Label - Other

Source Name

15,870

AOD

Alcohol and Other Drug Thesaurus, 2000

278

AOT

Authorized Osteopathic Thesaurus, 2003

13,255

CBO

Clinical Bioinformatics Ontology, 2011_02

1,104

CCS

Clinical Classifications Software, 2005

3,073

COSTAR

COSTAR, 1989-1995

16,572

CSP

CRISP Thesaurus, 2006

3,836

CST

COSTART, 1995

345

CTEP

Cancer Therapy Evaluation Program, 2004

7,278

DXP

DXplain, 1994

77,923

FMA

Foundational Model of Anatomy Ontology, 2_0

54,439

GO

Gene Ontology, 2010_04_01

5,602

HCPCS

Healthcare Common Procedure Coding System, 2010

7,483

HL7V3.0

HL7 Vocabulary Version 3.0, 2006_05

29,335

HUGO

HUGO Gene Nomenclature, 2010_05

11,532

ICD10

ICD10, 1998

1,004

ICD10AE

ICD10, American English Equivalents, 1998

97,631

ICD10CM

International Classification of Diseases, 10th Edition, Clinical Modification, 2010_03

181,116

ICD10PCS

ICD-10-PCS, 2009

20,779

ICD9CM

International Classification of Diseases, Ninth Revision, Clinical Modification, 2011

2,466

ICDO

International Classification of Diseases for Oncology, 3rd Edition, 2000

748

ICPC

International Classification of Primary Care, 1993

37,923

ICPC2ICD10ENG

ICPC2 - ICD10 Thesaurus, 200412

80,206

LNC

Logical Observation Identifier Names and Codes, 232

41

MCM

McMaster University Epidemiology Terms, 1992

331

MDBCAC

Mitelman Database of Chromosome Aberrations in Cancer, 2005_12

47,210

MDR

Medical Dictionary for Regulatory Activities Terminology, 14.0

1,897

MEDLINEPLUS

MedlinePlus Health Topics, 20080614

900

MGED

MGED Ontology, 131

310,485

MSH

Medical Subject Headings, 2011_2010_08_30

85,138

MTH

UMLS Metathesaurus, 2010AB

19,880

MTHFDA

Metathesaurus FDA National Drug Code Directory, 2010_08_02

337

MTHHH

Metathesaurus HCPCS Hierarchical Terms, 2010

16,081

MTHICD9

International Classification of Diseases, Ninth Revision, Clinical Modification, Metathesaurus additional entry terms, 2011

94

MTHICPC2ICD107B

ICPC2 - ICD10 Thesaurus, 7-bit Equivalents, 0412

76

MTHICPC2ICD10AE

ICPC2 - ICD10 Thesaurus, American English Equivalents, 0412

1,633

MTHMST

Metathesaurus Version of Minimal Standard Terminology Digestive Endoscopy, 2001

12,969

MTHSPL

Metathesaurus FDA Structured Product Labels, 2010_08_27

478,187

NCBI

NCBI Taxonomy, 2010_04_29

135

NCIMTH

NCI Metathesaurus

38,863

NDFRT

National Drug File, 2010_09_07

1,781

NPO

NPO: NanoParticle Ontology for Cancer Nanotechnology Research, 2010_10_31

63,924

OMIM

Online Mendelian Inheritance in Man, 2010_04_08

9,869

PDQ

Physician Data Query, 2010_08_06

2,041

PMA

Portfolio Management Application, 2010

268

PNDS

Perioperative Nursing Data Set, 2nd edition, 2002

940

QMR

Quick Medical Reference (QMR), 1996

30,033

RADLEX

RadLex, 3_33

227,498

RXNORM

RxNorm Vocabulary, 10AA_100907F

281,843

SNOMEDCT

SNOMED Clinical Terms

4,808

SPN

Standard Product Nomenclature, 2003

156

SRC

Metathesaurus Source Terminology Names

13,788

UMD

UMDNS: product category thesaurus, 2010

1,765

USPMG

USP Model Guidelines, 2004

61,191

UWDA

University of Washington Digital Anatomist, 1.7.3

26,546

VANDF

Veterans Health Administration National Drug File, 2010_07_26

11 - User Profiles - NCI

Some significant examples of collaborations and use of EVS resources and services are briefly outlined in this section covering NCI, and in later sections covering NIH, government and standards organizations, and other organizations in the cancer research and biomedical community. This section includes the following NCI profiles:

NCI Division of Cancer Biology (DCB)

EVS has supported DCB research on mouse and other animal models of cancer for more than 10 years. EVS has helped develop and maintain accurate coding and classification terminology for animal models, and has worked with NCI and community partners to develop accurate mappings between terminologies currently in use. Key NCI components of this effort are described here, while community partners are described later 14 - User Profiles - Broader Community.

Mouse Models of Human Cancer Consortium (MMHCC) was established by NCI in 1999 to accelerate the development and validation of mouse models by the scientific community. When the MMHCC was initiated, one of the early projects was to create a repository of curated information about animal models that have been employed in cancer studies, called the Cancer Models Database (now caMOD). EVS staff participated in developing the classifications of mouse diagnoses used for annotating the mouse models, and provided support for additional terminology such as strains and anatomy. All of this terminology has been incorporated into NCI Thesaurus (NCIt), used by caMOD and other users through both browser and programming interfaces. caMOD annotates information with NCIt terminology, and uses the LexEVS API directly to generate anatomy and diagnosis tree hierarchies. 400 concepts were added or updated for caMOD in the last-recorded six month period.

EVS has supported periodic updates to this animal model terminology, and has extended terminology support to cover rats and zebrafish, using existing community standards where available:

  • International Harmonization of Rat Nomenclature (RENI) was used as the foundation for the Terminology of Rat Pathologic Diagnoses in NCI Thesaurus (NCIt).
  • Zebrafish Information Network (ZFIN) zebrafish anatomy is provided as a standalone terminology in EVS systems including the NCI Term Browser.

MMHCC has merged with the Mouse Repository at Frederick National Research Laboratories, which provides mouse cancer models and associated strains, live and cryopreserved. Additional future support for updating the diagnosis terminology to reflect new models is anticipated.

For more information, visit the NCI eMICE website.

EVS Related References

  1. Bodenreider O, Hayamizu TF, Ringwald M, De Coronado S, Zhang S.
    Of mice and men: aligning mouse and human anatomies.
    AMIA Annu Symp Proc. 2005:61-5. PubMed PMID: 16779002; PubMed Central PMCID: PMC1560846. [PubMed]
  2. Hayamizu TF, de Coronado S, Fragoso G, Sioutos N, Kadin JA, Ringwald M.
    The mouse-human anatomy ontology mapping project.
    Database (Oxford). 2012 Mar 20;2012:bar066. Print 2012. PubMed PMID: 22434834. [PubMed] [Full Text]
  3. Kogan SC, Ward JM, Anver MR, Berman JJ, Brayton C, Cardiff RD, Carter JS, de Coronado S, Downing JR, Fredrickson TN, Haines DC, Harris AW, Harris NL, Hiai H, Jaffe ES, MacLennan IC, Pandolfi PP, Pattengale PK, Perkins AS, Simpson RM, Tuttle MS, Wong JF, Morse HC 3rd; Hematopathology subcommittee of the Mouse Models of Human Cancers Consortium.
    Bethesda proposals for classification of nonlymphoid hematopoietic neoplasms in mice.
    Blood. 2002 Jul 1;100(1):238-45. PubMed PMID: 12070033. [PubMed]

NCI Division of Cancer Control and Population Sciences (DCCPS)

DCCPS collaboration includes several terminology projects related to cancer classification, drugs, chemotherapy regimens, and statistics. DCCPS is currently launching a new initiative on standardized terminology for population health data collection.

Portfolio Management Application (PMA-DCCPS): EVS has worked with DCCPS for a number of years to integrate PMA-DCCPS grant coding terminology with other EVS resources. NCIt now includes more than 1,500 concepts for PMA grant coding. PMA terms are also inserted and appear in the NCI Metathesaurus as a separate terminology.

NCI Division of Cancer Prevention (DCP)

DCP has used EVS, and worked consistently with EVS, since 2002, including the following collaborations and uses:

  • Use of NCIt agent information in DCP systems.
  • Joint work on improved coverage of nutritional and other preventive agents.
  • Joint work in wider working groups and other initiatives to harmonize broader NCI agent and adverse event coding practices.

EVS Related References

  1. Kaefer CM, Milner JA.
    The role of herbs and spices in cancer prevention.
    J Nutr Biochem. 2008 Jun;19(6):347-61. Review. PubMed PMID: 18499033; PubMed Central PMCID: PMC2771684. [PubMed] [PubMed Central]

NCI DCP and DCTD

EVS staff provides terminology support for the Community Clinical Oncology Program (CCOP), including concepts for use in coding four (4) major clinical trials involving 85,000 patients. An estimated total of 500 studies use NCI Thesaurus terminology.

NCI Division of Cancer Treatment and Diagnosis (DCTD)

Cancer Therapy Evaluation Program (CTEP)

The Cancer Therapy Evaluation Program (CTEP) has had many and sustained collaborations with EVS from 1999 onwards, including:

  • Use of NCI Thesaurus (NCIt) terminology for protocol abstraction.
  • Harmonization and use of NCIt drug and molecular target information in CTEP systems.
  • Harmonization and integration of CTEP disease classification with NCIt, PDQ and MedDRA, involving several detailed comparative mappings and analyses as well as concept based mappings in the NCI Metathesaurus (NCIm).
  • Joint development, together with other partners, of the redesigned Common Terminology Criteria for Adverse Events (CTCAE) v.4, which has been widely adopted since its release in 2010 (see the detailed description below).

Common Terminology Criteria for Adverse Events (CTCAE)

CTCAE, created by CTEP in 1983, is used throughout the entire oncology community as the standard classification and severity grading scale for adverse events in cancer therapy clinical trials and other oncology settings. It is also used in a number of non-oncology trials and settings.

Version 4, released in May 2009, is a major update based on extensive international participation by stakeholders and experts. It is harmonized with MedDRA at the Adverse Event (AE) level, includes revised AE terms and severity indicators to reflect clinical effects identified with current oncology interventions, and was selected as a caBIG® vocabulary standard. This version is used by more than 50 academic and research organizations, as well as many commercial and non-profit organizations. Five different Apple applications utilize the NCI Thesaurus version of the CTCAE data, and cite NCIt as the source.

CTCAE is designed to integrate into information networks for safety data exchange, and plays a major role in data management for AE data collection, analysis, and patient outcomes associated with cancer research and care. EVS played a central role in designing and managing this effort, working closely with CTEP, DCP, caBIG®, the FDA, and many participants from the broader community. The revision was developed and deployed using various EVS tools including the BiomedGT Wiki, Protégé editing tools, LexEVS terminology server, NCI Term Browser, and the EVS ftp site .

Cancer Diagnosis Program (CDP) - Diagnostics Evaluation Branch

  • EVS is supporting gene nomenclature used in a new CDP Biomarker project, including updating Human Genome Organisation (HUGO) Gene Nomenclature Committee (HGNC) terminology in the EVS servers so it is accessible to CDP curators. See HGNC on NCI Term Browser.
  • In an extension to this project, EVS will be creating gene and protein sequence variations in the NCIt, according to HGVS guidelines, for genes/proteins of therapeutic interest as requested by CDP. The initial request is 150 genes (in progress), with about 180 protein variants, 300 gene variants, 300 fusion genes, and 300 fusion proteins.
  • This work is to support creation of an application to retrieve content from the NIH Clinical Trial Database into the NCI Clinical Trials Reporting Program (CTRP), and is being done jointly with DCTD, NCI/OCPL, NCI/CCR and NIH/NLM.

EVS Related References

  1. Chen AP, Setser A, Anadkat MJ, Cotliar J, Olsen EA, Garden BC, Lacouture ME.
    Grading dermatologic adverse events of cancer treatments: the Common Terminology Criteria for Adverse Events Version 4.0.
    J Am Acad Dermatol. 2012 Nov;67(5):1025-39. doi: 10.1016/j.jaad.2012.02.010. Epub 2012 Apr 11. PubMed PMID: 22502948. [PubMed]
  2. Cimino JJ, Hayamizu TF, Bodenreider O, Davis B, Stafford GA, Ringwald M.
    The caBIG terminology review process.
    J Biomed Inform. 2009 Jun;42(3):571-80. Epub 2008 Dec 25. PubMed PMID: 19154797; PubMed Central PMCID: PMC2729758. [PubMed]

NCI Division of Extramural Activities (DEA)

DEA is supported by EVS, which helps collect, develop and map grant-related terminology, including representing NCI in the NIH Research, Condition and Disease Categorization (RCDC) effort.

NCI Office of Communications and Public Liaison (OCPL)

OCPL co-managed EVS with CBIIT until late 2007, and has continued as an important partner since that time. Some key areas of ongoing collaboration are:

  • PDQ Terminology has been used for decades to code cancer clinical trials and other NCI scientific and public information resources, including NCI's Clinical Trials Reporting Program (CTRP) initiative. EVS has used PDQ terminology as a core component of its NCI Thesaurus (NCIt) reference terminology, and has worked to harmonize with and add to PDQ terminology even as NCIt was extended in many ways not required for PDQ itself. EVS took over and carries on efforts to harmonize and cross-link both PDQ terminology and NCIt with other NCI coding terminologies including CTEP disease and adverse event terminologies, NCI Developmental Therapeutics Program drug terms, and terminology from DCCPS and DCP. EVS continues to work with OCPL on strengthening PDQ terminology as a coding and information retrieval resource. See PDQ on NCI Term Browser and the PDQ to NCIt Mapping.
  • NCI Drug Dictionary provides technical definitions, alternate names, and links to related information for more than 3,000 agents that are being used in the treatment of patients with cancer or cancer-related conditions. Each entry includes a link to a more detailed entry in NCIt, which provides the information presented, as well as links to lists of open and closed cancer clinical trials on NCI's Web site, Cancer.gov. Each month, the NCI Drug Dictionary is used by over 30,000 unique visitors who view more than 65,000 pages.
  • Cancer.gov Database of Cancer Clinical Trials is updated daily and covers over 11,000 clinical trials now accepting participants, plus more than 25,000 others that are no longer recruiting. NCIt drug terminology is used to index PDQ trials, and EVS has contributed to the development of PDQ Terminology used to code cancers and related conditions, procedures, and chemotherapy regimens.
  • NCI Dictionary of Cancer Terms defines more than 7,000 cancer and biomedical terms in non-technical language. Terms and definitions are reviewed by a multidisciplinary panel of reviewers, and approximately 50 new and 50 revised terms are included each month. The dictionary is available as a stand-alone resource on every Cancer.gov Web page, and is widely used by other institutions and Web sites. Its contents are integrated into both NCI Thesaurus and NCI Metathesaurus, providing an important resource especially for non-technical users.

EVS Related References

  1. De Coronado S, Haber MW, Sioutos N, Tuttle MS, Wright LW.
    NCI Thesaurus: Using Science-Based Terminology to Integrate Cancer Research Results.
    Proceedings of the 11th World Congress on Medical Informatics (Medinfo 2004) Amsterdam: IOS Press 2004, pp. 33-37. Also cited as: Stud Health Technol Inform. 2004;107(Pt 1):33-7. [PubMed]
  2. Hubbard SL.
    Information systems in oncology.
    Cancer: Principles & Practice of Oncology, 6th ed. Devita VT, Hellman S, Rosenberg SA, eds. Philadelphia: Lippincott Williams & Wilkins; 2001. pp. 3135–46.
  3. Hubbard SM, Setser A.
    The Cancer Informatics Infrastructure: a new initiative of the National Cancer Institute.
    Semin Oncol Nurs. 2001 Feb;17(1):55-61. Review. PubMed PMID: 11236366. [PubMed] [Full Text]
  4. Sioutos N, De Coronado S, Haber MW, Hartel FW, Shaiu WL, Wright LW.
    NCI Thesaurus: A Semantic Model Integrating Cancer-Related Clinical and Molecular Information.
    Journal of Biomedical Informatics 2007 Feb;40(1):30-43. Epub 2006 Mar 15. [PubMed]

Other NCI User Profiles

Cooperative Human Tissue Bank (CHTB):  EVS has supported terminology creation and editing for this network of research institutions, established by the NCI Cancer Diagnosis Program in 1987 and now including six divisions located at Vanderbilt, U. Penn, UAB School of Medicine, Nationwide Children's Hospital, Ohio State, University of Virginia. CHTB hosts NCI funded tissue facilities and services providing remnant human tissue and fluids from routine procedures to investigators. There is active use by 14 academic and research organizations and eight (8) commercial organizations. EVS staff assisted with matching up terminology used by these groups to NCIt terminology, creating new NCIt concepts and definitions as needed.

Cancer Central Clinical Database (C3D): EVS provides support for C3D, largely through providing the new and updated terminology and definitions for case report forms. C3D currently supports electronic submission of clinical trials data to the NCI Clinical Data System (CDS) and the Clinical Trials Monitoring Service (CTMS/Theradex).

OPEN (Oncology Patient Enrollment Network): This web-based registration system for patient enrollments onto NCI-sponsored Cooperative Group clinical trials is a highly active project; most terminology supplied for this is standard demographic terminology.

12 - User Profiles - NIH

NCI EVS works closely with other members of the NIH community to develop shared terminology resources and standards, improving the quality and efficiency of our shared research mission as well as the exchange and reuse of data. These noteworthy examples of collaboration and reuse within NIH are included in the following sections:

National Heart, Lung, and Blood Institute (NHLBI)

Since 2005, NHLBI and EVS have jointly developed terminology in NCIt for various projects including bone marrow transplant clinical trials and the Family Blood Pressure Program. Duke, through an NIH Roadmap project, the American College of Cardiology, and CDISC, have also collaborated with EVS to produce several cardiovascular data standard sets now in use at NHLBI, with a portion incorporated into CDISC SDTM.

EVS Related References

  1. Hicks KA, Tcheng JE, Bozkurt B, Chaitman BR, Cutlip DE, Farb A, Fonarow GC, Jacobs JP, Jaff MR, Lichtman JH, Limacher MC, Mahaffey KW, Mehran R, Nissen SE, Smith EE, Targum SL.
    2014 ACC/AHA Key Data Elements and Definitions for Cardiovascular Endpoint Events in Clinical Trials: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards).
    J Am Coll Cardiol. 2015, doi: 10.1016/j.jacc.2014.12.018. [Epub ahead of print] PubMed PMID: 25553722. [PubMed]
  2. Anderson HV, Weintraub WS, Radford MJ, Kremers MS, Roe MT, Shaw RE, Pinchotti DM, Tcheng JE.
    Standardized Cardiovascular Data for Clinical Research, Registries, and Patient Care: A Report from the Data Standards Workgroup of the National Cardiovascular Research Infrastructure Project.
    J Am Coll Cardiol. 2013 May 7;61(18):1835-46. doi: 10.1016/j.jacc.2012.12.047. Epub 2013 Mar 6. PubMed PMID: 23500238. [PubMed]

National Human Genome Research Institute (NHGRI)

The Cancer Genome Atlas (TCGA) is a joint effort between NCI and NHGRI, developing a database of the changes that occur in the genome, associated with a specific cancer type. TCGA creates a national network of research and technology teams, and provides a mechanism for pooling results; data are publicly available. 25 Studies are associated with this data, and data are being actively submitted or used by 16 academic and research organizations, 27 commercial organizations, and 17 non-profit organizations. EVS supports terminology for annotating the CDEs for tagging information and samples, mainly cell and specimen type, with information about tumor size, anatomic location, sample preparation, and patient demographics.

PhenX Exit Disclaimer logo was initiated by NHGRI in 2007 and has broad participation by other NIH institutes and the research community. PhenX initially prioritized 21 research domains relevant to genomics research and public health; EVS provides ongoing terminology support for these domains and related PhenX efforts. For more information, see the in the community profiles.

EVS Related References

  1. Deus HF, Veiga DF, Freire PR, Weinstein JN, Mills GB, Almeida JS.
    Exposing the cancer genome atlas as a SPARQL endpoint.
    J Biomed Inform. 2010 Dec;43(6):998-1008. PubMed PMID: 20851208; PubMed Central PMCID: PMC3071752. [PubMed]

National Institute of Allergy and Infectious Diseases (NIAID)

The NIAID Division of Allergy, Immunology, and Transplantation (DAIT) Immunology and Data Analysis Portal (ImmPort) (see https://immport.niaid.nih.gov/ ) is a long-term, sustainable data warehouse promoting reuse of immunological data generated by NIAID and NIAID-funded investigators.  ImmPort uses NCIt terminology and NCI Term Browser to define data concepts and data concept attributes with standard terms to make the ImmPort data model semantically interoperable with other data repositories..

National Institute of Child Health and Human Development (NICHD)

Starting in 2008, NICHD initiated an ongoing effort in collaboration with EVS to establish a core library of harmonized pediatric terminology in NCIt through the Pediatric Terminology Harmonization Initiative (detailed description). In 2013, several new expert working groups were established to extend coverage. This terminology is being developed by international teams of experts to support the acquisition, exchange, submission and archiving of clinical research data by pediatric clinical researchers and caregivers. Terminology developed through the Initiative is associated with 848 NICHD Clinical Trials, and is being used by 52 different academic and research organizations.

More than 6,500 NCIt concepts are used to specify coding standards for neonatal and perinatal research, neurological development, newborn screening, and pediatric adverse events, endocrinology, immunization, infectious disease, medical devices, nephrology, oncology, and rheumatology.

For more information, visit the NCI website pediatric terminology page.

EVS Related References

  1. Bonhoeffer J, Kochhar S, Hirschfeld S, Heath PT, Jones CE, Bauwens J, Honrado Á, Heininger U, Muñoz FM, Eckert L, Steinhoff M, Black S, Padula M, Sturkenboom M, Buttery J, Pless R, Zuber P; GAIA project participants.
    Global alignment of immunization safety assessment in pregnancy - The GAIA project.
    Vaccine. 2016 Dec 1;34(49):5993-5997. doi: 10.1016/j.vaccine.2016.07.006. PubMed PMID: 27751641. [PubMed]
  2. Gipson DS, Kirkendall ES, Gumbs-Petty B, Quinn T, Steen A, Hicks A, McMahon A, Nicholas S, Zhao-Wong A, Taylor-Zapata P, Turner M, Herreshoff E, Jones C, Davis JM, Haber M, Hirschfeld S.
    Development of a Pediatric Adverse Events Terminology.
    Pediatrics. 2017 Jan;139(1). Available online Dec 27, 2016. pii: e20160985. doi: 10.1542/peds.2016-0985. [Epub ahead of print] PubMed PMID: 28028203. [PubMed]
  3. Hirschfeld S, Songco D, Kramer BS, Guttmacher AE.
    National Children's Study: update in 2010.
    Mt Sinai J Med. 2011 Jan-Feb;78(1):119-25. doi: 10.1002/msj.20227. PubMed PMID: 21259268. [PubMed]
  4. Kahn MG, Bailey LC, Forrest CB, Padula MA, Hirschfeld S.
    Building a Common Pediatric Research Terminology for Accelerating Child Health Research.
    Pediatrics, 2014. Available online Feb 17, 2014. DOI: 10.1542/peds.2013-1504. [Online Exit Disclaimer logo ]
  5. Sward KA, Rubin S, Jenkins TL, Newth CJ, Dean JM; Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Collaborative Pediatric Critical Care Research Network (CPCCRN).
    Case Study: Semantic Annotation of a Pediatric Critical Care Research Study.
    Comput Inform Nurs. 2016 Mar;34(3):101-4. doi: 10.1097/CIN.0000000000000236. PubMed PMID: 26958992; PubMed Central PMCID: PMC4788017. [PubMed]

National Institute of Dental and Craniofacial Research (NIDCR)

Since 2008, NIDCR and EVS have jointly developed terminology in NCIt for dental treatments and medical procedures.

National Library of Medicine (NLM)

Since its inception, EVS has worked with NLM on a variety of terminology content and technology efforts. EVS licenses content from and harmonizes with NLM's Unified Medical Language System (UMLS), building NCIm by modifying and extending a subset of the UMLS Metathesaurus and using NLM editing software.

NCI is also a contributor to the UMLS, providing monthly builds of NCIt for inclusion in UMLS. (UMLS only publishes twice yearly; they pull whichever build is most current at the time they begin processing.) EVS also worked with NLM and other federal partners to develop the Federal Medication Terminologies framework for harmonized medication coding (see below). EVS staff provided start-up phrase dictionaries and search criteria for the PubMed Cancer Subset, and jointly maintains this content with NLM on an ongoing basis.

NLM's DailyMed gets over 10 million hits each month, and each of the SPL files in DailyMed uses terminology that is maintained by NCI. There are over 27,000 SPL files on DailyMed, and another 5,000 SPL files that are not on DailyMed. Each of these SPL files requires NCIt codes.

NLM studies on a variety of biomedical terminology, ontology, and informatics issues have increasingly taken NCIt and other EVS resources as an important focus and test case. NCIt is also making an increasingly important contribution to results obtained in NLM's natural language processing work.

EVS Related References

  1. Bodenreider O.
    Comparing SNOMED CT and the NCI Thesaurus through Semantic Web technologies.
    Proceedings of the Third International Conference on Knowledge Representation in Medicine (KR-MED 2008), 2008: p. 37-43. [PDF (CEUR Workshop Procs. v.410) Exit Disclaimer logo ]
  2. Bodenreider O.
    Biomedical ontologies in action: role in knowledge management, data integration and decision support.
    Yearbook of Medical Informatics (2008) pp.67-79.
  3. Bodenreider O, Hayamizu TF, Ringwald M, De Coronado S, Zhang S.
    Of mice and men: aligning mouse and human anatomies.
    AMIA Annu Symp Proc. 2005:61-5. PubMed PMID: 16779002; PubMed Central PMCID: PMC1560846. [PubMed]
  4. Burgun A, Bodenreider O.
    Issues in integrating epidemiology and research information in oncology: experience with ICD-O3 and the NCI Thesaurus.
    AMIA Annu Symp Proc. 2007 Oct 11:85-9. PubMed PMID: 18693803. [PubMed]
  5. Cimino JJ, Hayamizu TF, Bodenreider O, Davis B, Stafford GA, Ringwald M.
    The caBIG terminology review process.
    J Biomed Inform. 2009 Jun;42(3):571-80. Epub 2008 Dec 25. PubMed PMID: 19154797; PubMed Central PMCID: PMC2729758. [PubMed]
  6. Fung KW, Bodenreider O.
    Knowledge Representation and Ontologies.
    In: Richesson RL, Andrews JE, editors. Clinical research informatics. New York: Springer; 2012. Chapter 14, pp.255-275. [Springer Exit Disclaimer logo ] [PDF]
  7. Luciano JS, Andersson B, Batchelor C, Bodenreider O, Clark T, Denney CK, Domarew C, Gambet T, Harland L, Jentzsch A, Kashyap V, Kos P, Kozlovsky J, Lebo T, Marshall SM, McCusker JP, McGuinness DL, Ogbuji C, Pichler E, Powers RL, Prud'hommeaux E, Samwald M, Schriml L, Tonellato PJ, Whetzel PL, Zhao J, Stephens S, Dumontier M.
    The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.
    J Biomed Semantics. 2011 May 17;2 Suppl 2:S1. PubMed PMID: 21624155; PubMed Central PMCID: PMC3102889. [PubMed] [Free PMC Article]
  8. Mougin F, Bodenreider O.
    Auditing the NCI thesaurus with semantic web technologies.
    AMIA Annu Symp Proc. 2008 Nov 6:500-4. PubMed PMID: 18999265; PubMed Central PMCID: PMC2655981. [PubMed]
  9. Pathak J, Peters L, Chute CG, Bodenreider O.
    Comparing and evaluating terminology services application programming interfaces: RxNav, UMLSKS and LexBIG.
    J Am Med Inform Assoc. 2010 Nov-Dec;17(6):714-9. PubMed PMID: 20962136; PubMed Central PMCID: PMC3000749. [PubMed]
  10. Zhang S, Bodenreider O.
    Alignment of multiple ontologies of anatomy: deriving indirect mappings from direct mappings to a reference.
    AMIA Annu Symp Proc. 2005:864-8. PubMed PMID: 16779163; PubMed Central PMCID: PMC1560629. [PubMed]
  11. Zhang S, Bodenreider O.
    Experience in Aligning Anatomical Ontologies.
    Int J Semant Web Inf Syst. 2007;3(2):1-26. PubMed PMID: 18974854; PubMed Central PMCID: PMC2575410. [PubMed]

NIH Biomedical Translational Research Information System (BTRIS)

The NIH Clinical Center’s trans-NIH Biomedical Translational Research Information System (BTRIS) was started in 2008 to help investigators from the NIH Clinical Center, Institutes and Centers access clinical and research data, improve protocol reporting and data analysis, and reuse data to support new hypotheses and collaborations. EVS and BTRIS share the common goals of providing tools to enable data aggregation and query, cross-study comparisons, and translation research. EVS has provided both the software editing tools and initial NCIt content to launch the Research Entities Dictionary (RED), which combines the terminologies from the systems that contribute data to BTRIS. Both programs continue to collaborate on shared clinical content standards, best practices and systems, in order to better coordinate research information across NIH.

BTRIS provides clinical investigators with access to identifiable data for the subjects on their own active protocols, while providing all NIH investigators with access to de-identified data across all protocols. BTRIS provides users with advanced search, filtering, and aggregation methods to create data sets to support ongoing studies and stimulate ideas for new research. BTRIS contains subject data from CRIS/MIS (the Clinical Center Medical Information Systems) and research data from NIAID (Crimson), NIAAA, and NCI. Data are available from 1976 to the present.

BTRIS Data Access is the data repository where principal investigators or their designees create reports on their active protocols with identified subject data. Multiple reports are available in BTRIS and can easily be run by researchers through a series of prompts. Reports include the IRB Inclusion Enrollment Report, demographics, patient lists, laboratory and microbiology results, vital signs, medication orders and administration, diagnoses, and radiology reports (with links to images in the CC PACS system). BTRIS provides researchers with tools to generate reports for their protocols. Reports are customizable for the requirements of specific reporting agencies, etc.

BTRIS creates and uses a Research Entities Dictionary (RED) to standardize data formats and terminologies between data sources. With the use of RED codes, researchers can extract a comprehensive set of like data from disparate sources, and BTRIS tools support construction of comprehensive queries. Use of the RED also allows researchers to identify patients that meet multiple criteria relevant to the researcher’s interests. Work is ongoing to publish RED through EVS terminology servers and browsers.

For more information, visit the BTRIS website.

NIH Grants

NCIt is one of four NIH-approved terminologies for grant coding, and EVS has helped develop the Research, Condition and Disease Categorization (RCDC) grant coding system.

13 - User Profiles - Government and Standards Organizations

NCI EVS has extensive partnerships with government and standards organizations beyond NIH to develop terminology standards, content, technology, and operational support.  Such partnerships are designed to directly support NCI's cancer research mission, improving regulatory, federal and community practices in ways that contribute to the conduct and sharing of cancer research while also having a positive impact on the wider biomedical community. The following collaborations are included in this section:

U.S. Food and Drug Administration (FDA)

FDA has worked with EVS since 2001, with formal Memoranda of Understanding starting in 2004, to develop and harmonize terminology content, standards and systems in areas of mutual interest such as drugs, devices, patient safety, and clinical trials. FDA has chosen EVS and NCI Thesaurus (NCIt) for developing and publishing many important terminology sets; some 15,000 FDA terms in over 20 defined subsets are now maintained in NCIt and required for regulatory reporting and other purposes. These include:

  • Structured Product Labeling (SPL): Standard terminology for Drug Establishment Registration (Regulated Product Submission), Drug Listing and the Content of Labels. 16 NCIt subsets used for submission of proposed labeling by all manufacturers using electronic formats. There are 9,466 establishments from over 100 countries that use the SPL terminology in order to comply with federal regulations to list their products. There are approximately 40,000 subscribers to the FDA's SPL LISTSERV, and this is one of the primary mechanisms to inform users of changes to the SPL terminology that is maintained by NCI. FDA does not track the number of hits against the FDA Resources for Data Standards page and its sub pages, but they suspect that the number of hits is very high; when firms do not select the correct NCIt code, their submitted SPL file will not pass validation.
  • Unique Ingredient Identifier (UNII) codes are being developed by FDA to uniquely identify all ingredients used in marketed medications in the United States, as well as substances in biologics, foods and devices. Each UNII is assigned based on molecular structure or other immutable characteristics. FDA provides a full set of published UNII codes and a search page on a Web site now hosted by the National Library of Medicine (NLM) and updated approximately monthly.
     
    EVS collaborated with FDA on the launch and early publication of UNII codes. More than 12,000 UNII codes have been included in corresponding NCIt concepts, and more continue to be added each month, although NCIt no longer provides comprehensive representation of all UNII concepts. Most of these 12,000 concepts were included in NCIt because of their therapeutic and other interest for cancer and related research, and they are extensively annotated with definitions, chemical formulae, CAS registry numbers, synonyms, and other information to help support such research. Files providing UNIIs that have matching NCIt concept codes are available for download in Excel and text formats.
  • CDRH Device Event Problem Codes: NCIt subsets used for the reporting of medical device problems to FDA. CDRH is responsible for ensuring the safety and effectiveness of medical devices and eliminating unnecessary human exposure to man-made radiation from medical, occupational and consumer products. Approximately 200 organizations from some 3,000 different reporting locations report to the CDRH using NCIt terminology. CDRH receives approximately 35,000 submissions per month that are based on NCIt terminology.
  • Individual Case Safety Report (ICSR): NCIt subsets used for adverse event reporting. Proposed regulations for electronic submissions will create similar levels of use for these subsets.
  • eCTD (electronic Common Technical Documents): Standard terminology for regulatory forms required by the Center for Drug Evaluation and Research (CDER).
  • Drug Submissions: Beginning June 1, 2009, FDA no longer accepts paper submissions for drug establishment registration and listing unless a waiver is granted. Moving from a paper-based format to an electronic system will improve the timeliness and accuracy of the submissions. This is not possible without the terminology provided by NCIt. Of 22,246 New Drug related Submissions from October 2009 - September 2010, (63%) were in electronic format submissions that required terminology from NCIt.
  • Stability: NCIt provides terminology for FDA and HL7 Stability Data Standards, including human pharmaceuticals, animal drugs and medical devices that are in regulatory submissions, amendments, supplements and annual reports.

EVS resources and systems are also used in FDA efforts such as the Janus Clinical Trials Repository (CTR) Project , a standards-based repository of subject level clinical trial data to support regulatory review and patient centered outcomes research (PCOR).

For more information, visit the NCI website FDA terminology resources.

EVS Related References

  1. McCullough CE, Reed TL, Kaufman-Rivi D.
    A Tool to Analyze Medical Device Problems: The Food and Drug Administration Device Problem Codes.
    J Clinical Engineering. 2012 Apr/Jun;37(2):56–62. doi: 10.1097/JCE.0b013e31824c99f1 [Online Exit Disclaimer logo ]
  2. Reed TL, Kaufman-Rivi D.
    FDA adverse Event Problem Codes: standardizing the classification of device and patient problems associated with medical device use.
    Biomed Instrum Technol. 2010 May-Jun;44(3):248-56. PubMed PMID: 20715359. [PubMed]

Clinical Data Interchange Standards Consortium (CDISC)

CDISC is an international, non-profit organization that develops and supports global data standards for medical research. CDISC is working actively with EVS to develop and support controlled terminology for a wide spectrum of clinical and nonclinical studies.

CDISC terminology is being widely adopted as a standard for study coding and data submissions. In the United States, draft FDA guidance on regulatory submissions (see Study Data Technical Conformance Guide) recommends CDISC terminology as a set of controlled terms that meet the FDA requirements for the implementation of the CDISC standards. As part of the General Considerations of Controlled Terminology regarding CDISC, the Guide states: "Sponsors should use the terminologies and code lists in the CDISC Controlled Terminology, which can be found at the NCI (National Cancer Institute) Enterprise Vocabulary Services."

CDISC terminology goes through an extensive process of content development and public review before it is declared ready for release. All CDISC controlled terminology – more than 10,000 terms – is maintained and published as NCI Thesaurus (NCIt) subsets, as part of a partnership started in 2002. The main terminology efforts encompassed by the CDISC-EVS partnership are shown below:

  • Study Data Tabulation Model (SDTM) is an international standard for clinical research data, and is approved by the FDA and Japan’s Pharmaceuticals and Medical Devices Agency (PMDA) Exit Disclaimer logo as a standard electronic submission format. EVS maintains and distributes SDTM controlled terminology as part of NCIt. More information is available at CDISC's SDTM Web page Exit Disclaimer logo . SDTM has been downloaded more than 14,000 times in over 90 countries, primarily for institutional use. More than 270 commercial organizations use SDTM, as do numerous academic, non-profit and research organizations.
  • Questionnaire (QS) and Functional Test (FT) Terminology contains standardized, controlled terminology for commonly used questionnaires and functional tests in biomedical and therapeutic area research. EVS maintains and distributes Questionnaire controlled terminology as part of NCIt. More information is available at CDISC's Questionnaire Web page Exit Disclaimer logo . Questionnaire terminology can be used for both collection (CDASH) and submission (SDTM) data sets.
  • Clinical Data Acquisition Standards Harmonization (CDASH) develops clinical research study content standards in collaboration with sixteen partner organizations including NCI. EVS maintains and distributes CDASH controlled terminology, a subset of SDTM, as part of NCIt. More information is available at CDISC's CDASH Web page.
  • Analysis Data Model (ADaM) supports efficient generation, replication, review and submission of analysis results from clinical trial data. EVS maintains and distributes ADaM controlled terminology as part of NCIt. More information is available at CDISC's ADaM Web page Exit Disclaimer logo .
  • Standard for Exchange of Non-Clinical Data (SEND) extends Study Data Tabulation Model (SDTM) for non-clinical studies. SEND guides the organization, structure and format of standard nonclinical tabulation data sets for interchange between organizations such as sponsors and CROs and for submission to a regulatory authority such as the FDA. NCI EVS maintains and distributes SEND controlled terminology as part of NCIt. It now includes some 1,000 additional terms beyond the SDTM terminology that is also part of the SEND standard. More information is available at CDISC's SEND Web page Exit Disclaimer logo .

The CDISC Shared Health and Research Electronic Library (SHARE) project aims to create a global, electronically accessible library of CDISC standard metadata that can be used to improve biomedical research and its link with healthcare. The SHARE metadata repository (MDR) provides a dynamic environment where the relationships between and among CDISC metadata and controlled terminologies are published at a level of granularity that is user-defined and in forms that are both human and machine readable. The SHARE MDR is built using CDISC and BRIDG metadata and terminology that is coded and maintained in the EVS NCIt environment. More information is available at CDISC’s SHARE Web page Exit Disclaimer logo .

For more information, visit the NCI website CDISC terminology resources.

EVS Related References

  1. Anzai T, Kaminishi M, Sato K, Kaufman L, Iwata H, Nakae D.
    Responses to the Standard for Exchange of Nonclinical Data (SEND) in non-US countries.
    J Toxicol Pathol. 2015 Apr;28(2):57-64. doi: 10.1293/tox.2015-0007. Epub 2015 Apr 1. Review. PubMed PMID: 26028814; PubMed Central PMCID: PMC4444503. [PubMed][PDF Exit Disclaimer logo ]
  2. Elkin PL.
    Springer Terminology Related Standards Development.
    In Elkin PL, ed. Terminology and Terminological Systems, Ch.7 pp.107-123, Springer London, 2012. [Springer Exit Disclaimer logo ]
  3. Haber MW, Kisler BW, Lenzen M, Wright LW.
    Controlled Terminology for Clinical Research: A Collaboration between CDISC and NCI Enterprise Vocabulary Services.
    Drug Information Journal 2007;41(3):405-412. [PDF Exit Disclaimer logo ]
  4. Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ.
    Standardizing Data Exchange for Clinical Research Protocols and Case Report Forms: An Assessment of the Suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).
    J Biomed Inform. 2015 Jul 15. pii: S1532-0464(15)00133-1. doi: 10.1016/j.jbi.2015.06.023. [Epub ahead of print] PubMed PMID: 26188274. [PubMed]
  5. Jiang G, Evans J, Oniki TA, Coyle JF, Bain L, Huff SM, Kush RD, Chute CG.
    Harmonization of detailed clinical models with clinical study data standards.
    Methods Inf Med. 2015;54(1):65-74. doi: 10.3414/ME13-02-0019. Epub 2014 Nov 26. PubMed PMID: 25426730. [PubMed]
  6. Jiang G, Solbrig HR, Iberson-Hurst D, Kush RD, Chute CG.
    A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki.
    AMIA Summits Transl Sci Proc. 2010 Mar 1;2010:11-5. PubMed PMID: 21347136; PubMed Central PMCID: PMC3041544. [PubMed]
  7. Kaufman L, Gore K, Zandee JC.
    Data Standardization, Pharmaceutical Drug Development, and the 3Rs.
    ILAR Journal. 2016 Dec 31;57(2):109-119.
  8. Keenan CM, Baker J, Bradley A, Goodman DG, Harada T, Herbert R, Kaufmann W, Kellner R, Mahler B, Meseck E, Nolte T, Rittinghausen S, Vahle J, Yoshizawa K.
    International Harmonization of Nomenclature and Diagnostic Criteria (INHAND): Progress to Date and Future Plans.
    Toxicol Pathol. 2014 Dec 21. pii: 0192623314560031. [Epub ahead of print] PubMed PMID: 25530274. [PubMed]
  9. Keenan CM, Goodman DG.
    Regulatory Forum Commentary: Through the Looking Glass--SENDing the Pathology Data We Have INHAND.
    Toxicol Pathol. 2014 July, 42(5):807-810. Epub 2013 Apr 18. PubMed PMID: 23599411. [PubMed]
  10. Kush RD.
    Interoperability for the Learning Health System.
    Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. Grossmann C, Powers B, McGinnis JM eds. Institute of Medicine (2011) pp.108-114. [Online Exit Disclaimer logo ]
  11. Leroux H, Lefort L.
    Semantic enrichment of longitudinal clinical study data using the CDISC standards and the semantic statistics vocabularies.
    J Biomed Semantics. 2015 Apr 9;6:16. doi: 10.1186/s13326-015-0012-6. eCollection 2015. PubMed PMID: 25973166; PubMed Central PMCID: PMC4429421. [PubMed]
  12. Perrone RD, Neville J, Chapman AB, Gitomer BY, Miskulin DC, Torres VE, Czerwiec FS, Dennis E, Kisler B, Kopko S, Krasa HB, LeRoy E, Castedo J, Schrier RW, Broadbent S.
    Therapeutic Area Data Standards for Autosomal Dominant Polycystic Kidney Disease: A Report From the Polycystic Kidney Disease Outcomes Consortium (PKDOC).
    Am J Kidney Dis. 2015 Jun 15. pii: S0272-6386(15)00758-1. doi: 10.1053/j.ajkd.2015.04.044. [Epub ahead of print] PubMed PMID: 26088508. [PubMed]
  13. Wood F.
    The Standard for the Exchange of Nonclinical Data (SEND): History, Basics, and Comparisons with Clinical Data.
    PharmaSUG 2016 Conference Proceedings. Denver, Colorado, May 8-11, 2016. [PDF Exit Disclaimer logo ]

Coalition for Accelerating Standards and Therapies (CFAST)

CFAST is a joint initiative of the Clinical Data Interchange Standards Consortium (CDISC) and the Critical Path Institute (C-Path), with steering committee participation from the US Food and Drug Administration (FDA), the National Cancer Institute Enterprise Vocabulary Services (NCI EVS) and TransCelerate BioPharma (TCB). Its purpose is to create therapeutic area-specific data standards to support clinical research in areas of particular importance to public health. Section XII of the Prescription Drug User Fee Act V (PDUFA V) provides the directive for FDA support for this Data Standards for Therapeutic Areas initiative. More information is available on the CFAST Web pages of CDISC Exit Disclaimer logo and C-Path Exit Disclaimer logo , and on the FDA Therapeutic Areas Web page.

Therapeutic area teams follow a CFAST-approved process that incorporates stakeholder inputs and public review to produce therapeutic area user guides, questionnaire supplements and controlled terminology. Controlled terminology goes through an extensive process of content development and public review before it is declared ready for release. All controlled terminology developed by the therapeutic area teams is published as part of the CDISC controlled terminology standards.

For more information on CDISC controlled terminology, visit the NCI website CDISC Terminology resources.

EVS Related References

  1. Haber MW, Kisler BW, Lenzen M, Wright LW.
    Controlled Terminology for Clinical Research: A Collaboration between CDISC and NCI Enterprise Vocabulary Services.
    Drug Information Journal 2007;41(3):405-412. [PDF Exit Disclaimer logo ]
  2. U.S. Food and Drug Administration.
    FDA Therapeutic Area Standards (TAS) Initiative Project Plan, Version 2.0.
    June 2014. [PDF]

National Council of Prescription Drug Providers (NCPDP)

NCPDP is a not-for-profit, ANSI-accredited, Standards Development Organization with over 1,600 members representing virtually every sector of the pharmacy services industry. NCPDP creates and promotes the transfer of data related to medications, supplies, and services within the healthcare system through the development of standards and industry guidance. In 2009, NCPDP decided to partner with EVS to use NCIt subsets to support two of those standards, employed by some 200 vendors serving approximately 15,000 pharmacies nationwide:

  • NCPDP SCRIPT Standard supports messages for new prescriptions, prescription changes, refill requests, prescription fill status notification, prescription cancellation, medication history, and transactions for long term care environments. Many large providers such as First DataBank and Surescripts, the nation's largest e-prescriber, use this standard. Surescripts alone connects thousands of pharmacies across the US, and is connected to the largest network of payers and Medicaid Fee for Service payers nationwide.
  • NCPDP Telecommunication Standard supports the electronic communication of claims and other transactions between pharmacy providers, insurance carriers, third party administrators, and other responsible parties. It is the standard used for eligibility, claims processing, reporting and other pharmacy industry communications, as designated in HIPAA. More than 4 billion claims are processed each year using this standard.

For more information, visit the NCI website NCPDP Terminology resources.

EVS Related References

  1. Liu H, Burkhart Q, Bell DS.
    Evaluation of the NCPDP Structured and Codified Sig Format for e-prescriptions.
    J Am Med Inform Assoc. 2011 Sep 1;18(5):645-51. Epub 2011 May 25. PubMed PMID: 21613642; PubMed Central PMCID: PMC3168301. [PubMed]

Federal Medication Terminologies (FMT)

Work started in 2002 as an interagency collaboration between NCI EVS, FDA, VHA, and NLM – joined later by AHRQ, CMS, DoD, and EPA – to improve the exchange and public availability of medication information with coordinated development of terminology standards. The initial FMT terminology set has been endorsed by U.S. Federal standards efforts including the National Committee on Vital and Health Statistics (NCVHS), Consolidated Health Informatics (CHI), the Healthcare Information Technology Standards Panel (HITSP), and the Office of the National Coordinator for Health Information Technology (ONC) within the Department of Health and Human Services (HHS).

For more information, visit the NCI Website Federal Medication Terminologies resources.

U.S. Environmental Protection Agency (EPA)

EPA joined in the Federal Medication Terminologies (FMT) collaboration.  EPA has also developed an EPA Science Vocabulary that utilizes substantial content from NCI Thesaurus, particularly for definitions, in addition to content from numerous EPA glossaries and other documents, and from other terminologies such as the Human Disease Ontology.  It will be made publicly available in December 2014.

Veterans Health Administration (VHA)

VHA has worked closely with EVS since 2002. Under an ongoing Memorandum of Understanding, VHA works with EVS on collaborative terminology policy and products, including content collaboration and publication of the VA's National Drug File Reference Terminology (NDF-RT), used by both agencies as a drug information reference resource.

The VHA also performed a thorough comparison of the functional capabilities of LexEVS to those of other terminology servers. The VA provided the results to the VKC, which transformed the matrix into a document that can be used by potential adopters of LexEVS as they evaluate the capabilities of the system. This is an example of a valuable contribution from the community that is not code-based; in an open source model, contributions of documentation can be as important as contributions of code.

Medical Dictionary for Regulatory Activities (MedDRA)

MedDRA is an international terminology for coding and regulatory reporting of drug and device adverse events. MedDRA is an International Conference on Harmonisation of Technical Requirements for Registration of Pharmaceuticals for Human Use (ICH) standard, adopted by FDA and many other agencies. MedDRA is used by more than 3,000 regulatory, industry and academic subscribers from 60 countries, and has been translated into 11 languages.

EVS manages the MedDRA license for NIH, and publishes multiple versions through LexEVS, the NCI Term Browser, and other means. EVS maintains multiple versions of MedDRA on its servers and browsers to support validation and interpretation of data encoded with those versions.

Over 10 years, EVS has performed numerous mapping comparisons between MedDRA and NCI terminologies including NCI Thesaurus (NCIt), PDQ, CTEP SDC, and CTCAE, as part of ongoing efforts to promote compatibility and data translation between these sources. NCI Metathesaurus maintains mappings between MedDRA and more than 70 other biomedical terminologies, providing a rich source of additional description of MedDRA terms and supporting data translation and analysis.

EVS and the MedDRA Maintenance and Support Services Organization (MSSO) Exit Disclaimer logo are working with the UK Medicines and Healthcare products Regulatory Agency (MHRA) Exit Disclaimer logo on use of the EVS Mapping Tool to develop an initial proof-of-concept mapping from SNOMED CT to MedDRA, focusing on high frequency adverse event terms to test the possibility of automated conversion of EHR data to support more proactive identification of potential signals with drugs that have gained marketing approval.

EVS and the MSSO have worked with NICHD, FDA, and other partners in developing a specialized set of pediatric adverse event terminology for use in research and care, as a part of the larger Pediatric Terminology Subset in NCIt and also for inclusion and distribution as part of MedDRA. For more information, visit the NCI website pediatric terminology page.

EVS has made numerous contributions to updates of MedDRA terminology. EVS conducted a comprehensive review of the over 8,500 terms in MedDRA's Neoplasm classification, suggesting changes initially presented to a special Blue Ribbon Panel meeting in 2011. NCIt is used as the primary reference terminology for updates to MedDRA neoplastic terminology.  The MedDRA Browser now includes more than 10,000 link-outs to full-text term definitions in NCIt.

World Health Organization (WHO)

The International Classification of Diseases (ICD) is the global standard diagnostic classification for all general epidemiological and many health management purposes, and has many clinical uses. WHO work on the International Classification of Diseases 11th revision (ICD-11) is using NCI Thesaurus (NCIt) as an important source of cancer-related terminology, relationships, and other features such as definitions. EVS has helped facilitate access to and reuse of NCIt content, as well as expert review and suggestions on some draft ICD-11 content; the oncology-specific ICD-O is also being influenced by NCIt's in-depth characterization of cancers and other neoplasms.

WHO is also taking advantage of EVS-supported open source terminology tooling in its work. In June 2009, WHO requested a Classification Markup Language (ClaML) LexEVS data processing program that could be used to render ICD-10 in preparation for, and as the foundation of, ICD-11. Stanford University's Protégé and related terminology editing tools are also a vital component of WHO efforts.

Health Level 7 (HL7)

EVS supports terminology content for the Regulated Clinical Research Information Management (RCRIM) committee of HL7, and for other committees as appropriate including Patient Safety, Pharmacy, Clinical Genomics, and the Clinical Interoperability Council.

CareLex

CareLex™ (http://www.carelex.org/) is a publicly funded not-for-profit enterprise working to improve information interoperability in health sciences to accelerate delivery of new therapies to patients.  To achieve this, CareLex actively partners with the biopharmaceutical industry, researchers, contract research organizations (CROs), technology experts, allied professionals, and government regulators to develop and manage open source technologies and advance global standards for clinical trials data interoperability.

EVS has worked with CareLex since 2013 to help develop and publish terminology for their electronic Trial Master File (eTMF) Standards Initiative.  This terminology is maintained and distributed as part of NCI Thesaurus (NCIt), and can be found at http://evs.nci.nih.gov/ftp1/CareLex/About.html .

The Taiwan Cancer Registry

The Taiwan Cancer Registry, a population-based cancer registry founded in 1979, uses NCI Thesaurus terminology. Hospitals with greater than 50-bed capacity that provide outpatient and hospitalized cancer care are recruited to participate in reporting all newly diagnosed malignant neoplasms to this registry.

EVS Related References

  1. Chen S, Hsu C.
    The TCR Cancer Registry Repository for Annotating Cancer Data.
    Emergency Management and Management Sciences (ICEMMS), 2011 2nd IEEE International Conference on, 2011 Aug 8-10:297-300. [IEEE Exit Disclaimer logo ]

14 - User Profiles - Broader Community

EVS has worked in partnership with and been used by many other organizations in the cancer research and biomedical community. Some noteworthy examples are described briefly in this section as identified in the list that follows. Single-institution efforts are described first, followed by thematic efforts that span multiple institutions.

Some projects not covered in this section have put listings on the NCBO BioPortal detail page for NCI Thesaurus Exit Disclaimer logo .

Institutional

American Association for Cancer Research (AACR)

AACR, founded in 1907, fosters research in cancer and related biomedical science, including advances in the understanding of cancer etiology, prevention, diagnosis, and treatment throughout the world.  A key recent initiative related to EVS is GENIE.

Genomics, Evidence, Neoplasia, Information, Exchange (GENIE)

AACR created GENIE Exit Disclaimer logo as a multi-phase, multi-year, international cancer registry built by sharing clinical cancer sequencing data from eight international institutions that are global leaders in genomic sequencing for clinical utility. An initial set of nearly 19,000 genomic records was released in January 2017 and can be directly accessed through cBioPortal. The primary cancer diagnosis code is based on OncoTree, which is mostly linked to NCI Thesaurus (NCIt) and NCI Metathesaurus (NCIm) codes.

American College of Cardiology (ACC)

American College of Cardiology (ACC) and EVS have worked together for several years on a number of projects, some also involving NHLBI, CDISC and Duke University. EVS worked with ACC and CDISC to develop 389 terms for the ACC/CDISC Cardiovascular Disease Therapeutic Area standard, bundled in 30 new codelists as well as a number of existing codelists. ACC made use of 157 existing NCI Thesaurus terms and added 232 new ones, while EVS added 194 ACC concept definitions as NCI preferred definitions.

EVS Related References

  1. Anderson HV, Weintraub WS, Radford MJ, Kremers MS, Roe MT, Shaw RE, Pinchotti DM, Tcheng JE.
    Standardized Cardiovascular Data for Clinical Research, Registries, and Patient Care: A Report from the Data Standards Workgroup of the National Cardiovascular Research Infrastructure Project.
    J Am Coll Cardiol. 2013 May 7;61(18):1835-46. doi: 10.1016/j.jacc.2012.12.047. Epub 2013 Mar 6. PubMed PMID: 23500238. [PubMed]

American Society of Clinical Oncology (ASCO)

ASCO brings together more than 40,000 oncology health care professionals in many activities related to EVS, most notably CancerLinQ.

Cancer Learning Intelligence Network for Quality (CancerLinQ)

ASCO created (CancerLinQ Exit Disclaimer logo ) to provide a big data based learning health care platform for oncology, now encompassing more than 1,000,000 patient records. NCI Metathesaurus is used as the content knowledge source for processing and standardizing these data across EHR systems.

EVS Related References

  1. Schilsky RL.
    Finding the Evidence in Real-World Evidence: Moving from Data to Information to Knowledge.
    J Am Coll Surg. 2017 Jan;224(1):1-7. doi: 10.1016/j.jamcollsurg.2016.10.025. PubMed PMID: 27989954. [PubMed]
  2. Schilsky RL, Miller RS.
    Creating a Learning Health Care System in Oncology.
    Oncology Informatics: Using Health Information Technology to Improve Processes and Outcomes in Cancer. Hesse BW, Ahern DK, Beckjord E, eds. Elsevier Academic Press (2016), pp. 3–21.
  3. Visvanathan K, Levit LA, Raghavan D, Hudis CA, Wong S, Dueck A, Lyman GH.
    Untapped Potential of Observational Research to Inform Clinical Decision Making: American Society of Clinical Oncology Research Statement.
    J Clin Oncol. 2017 Mar 30:JCO2017726414. doi: 10.1200/JCO.2017.72.6414. [Epub ahead of print] PubMed PMID: 28358653. [PubMed]

Catalogue of Somatic Mutations in Cancer (COSMIC)

COSMIC Exit Disclaimer logo provides a very broad, high-resolution resource for exploring the impact of somatic mutations in human cancer. COSMIC has translated its custom tumor classification into NCI Thesaurus (NCIt), "selected as the highest-resolution public ontology across cancer diseases." (Forbes et al. 2016).

EVS Related References

  1. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L, Stefancsik R, Harsha B, Kok CY, Jia M, Jubb H, Sondka Z, Thompson S, De T, Campbell PJ. COSMIC: somatic cancer genetics at high-resolution. Nucleic Acids Res. 2016 Nov 28. pii: gkw1121. [Epub ahead of print] PubMed PMID: 27899578. [PubMed]

Clinical Genome Resource (ClinGen)

ClinGen Exit Disclaimer logo launched in 2013 as an NIH supported partnership among public, academic, and private institutions dedicated to building an authoritative central resource that defines the clinical relevance of genes and variants for use in precision medicine and research. The ClinGen Somatic Cancer Working Group developed with ClinVar and multiple stakeholders a consensus set of minimal variant level data, recommending that Cancer Type be coded using either NCI Thesaurus or Oncotree, which itself includes the NCI Thesaurus code.

EVS Related References

  1. Ritter DI, Roychowdhury S, Roy A, Rao S, Landrum MJ, Sonkin D, Shekar M, Davis CF, Hart RK, Micheel C, Weaver M, Van Allen EM, Parsons DW, McLeod HL, Watson MS, Plon SE, Kulkarni S, Madhavan S; ClinGen Somatic Cancer Working Group.
    Somatic cancer variant curation and harmonization through consensus minimum variant level data.
    Genome Med. 2016 Nov 4;8(1):117. PubMed PMID: 27814769; PubMed Central PMCID: PMC5095986. [PubMed]

Duke University

Duke University and EVS have collaborated for several years on various projects. As one example, the Research Informatics group of the Duke Clinical Research Institute (DCRI) has used vocabulary services from the EVS LexEVS terminology server in adding standard metadata to the Cardiovascular DAM. Duke and EVS have worked together with other partners such as CDISC, NHLBI, and the American College of Cardiology, in the development of shared terminology and other standards involving clinical trials, case report forms, cardiology, tuberculosis, and other content.

EVS Related References

  1. Anderson HV, Weintraub WS, Radford MJ, Kremers MS, Roe MT, Shaw RE, Pinchotti DM, Tcheng JE.
    Standardized Cardiovascular Data for Clinical Research, Registries, and Patient Care: A Report from the Data Standards Workgroup of the National Cardiovascular Research Infrastructure Project.
    J Am Coll Cardiol. 2013 May 7;61(18):1835-46. doi: 10.1016/j.jacc.2012.12.047. Epub 2013 Mar 6. PubMed PMID: 23500238. [PubMed]

Emory University

Emory University has deployed and extended several EVS resources. Emory is using LexEVS to develop and host local ontologies. Uses include terminology support for an analytic data warehouse, which incorporates custom patient classes defined using ICD-9 codes.

EVS Related References

  1. Vergara-Niedermayr C, Wang F, Pan T, Kurc T, Saltz J.
    Semantically Interoperable XML Data.
    Int. J. Semantic Computing. 2013 Sept;7(3). DOI: 10.1142/S1793351X13500037 [Online Exit Disclaimer logo ]
  2. Vergara-Niedermayr C, Wang F, Pan T, Kurc T, Saltz J.
    Semantically Interoperable XML Data.
    Emory University Center for Comprehensive Informatics Technical Report CCI-TR-2012-1, January 12, 2012. [Emory Exit Disclaimer logo ]
  3. Zheng S, Wang F, Lu J, Saltz J.
    Enabling Ontology Based Semantic Queries in Biomedical Database Systems.
    Emory University Center for Comprehensive Informatics Technical Report CCI-TR-2012-3, March 20, 2012. [Emory Exit Disclaimer logo ]

European Bioinformatics Institute (EBI)

The European Bioinformatics Institute (EBI) Experimental Factor Ontology (EFO Exit Disclaimer logo ) provides a systematic description of many experimental variables, and is used to support a number of EBI databases as well as the National Human Genome Research Institute (NHGRI) Genome-Wide Association Study (GWAS) catalogue. It combines parts of several biological ontologies, covering domains such as anatomy, disease and chemical compounds. The EFO reuses cancer-related terminology from the NCI Thesaurus (NCIt).

EVS Related References

  1. Malone J, Rayner TF, Bradley XZ, Parkinson H.
    Developing an application focused experimental factor ontology: embracing the OBO Community.
    Bio-Ontologies SIG, ISMB 2008 Toronto Canada, 20 July 2008. [PDF Exit Disclaimer logo ]

General Electric (GE)

GE is developing a platform called Qualibria, which includes LexEVS as a terminology server. VKC has been working with GE since 2008. As part of the collaboration, GE created an extension to the LexEVS 5.1 API based on the Common Terminology Services specification. GE made the code for that extension available to the community via the VKC web site, and bundled EVS-supported open source technology into GE's commercial healthcare product.

Georgetown University

Georgetown uses LexEVS and other EVS resources for its cancer Bench-to-Bedside (caB2B) project and other translational medicine activities. Both a local LexEVS installation and NCI's production LexEVS servers provide terminology support for this project.

Jackson Laboratory

The Jackson Laboratory has been a close partner with NCI – and EVS in particular – for over 10 years. They have been closely involved in the MMHCC and other initiatives described in the NCI Division of Cancer Biology (DCB) section of 11 - User Profiles - NCI. EVS publishes their Adult Mouse Anatomy (MA) on NCI servers and browsers (see NCI Term Browser).

As part of the Mouse-Human Anatomy Project (MHAP), anatomy terms in MA and the NCIt human anatomy were compared and harmonized, and a formal mapping was jointly created and validated in 2006; this mapping has been recently updated and published as a mapping accessible through the LexEVS server APIs and NCI Term Browser (see MA to NCIt Mapping). As both anatomical ontologies are being used to annotate different types of research data for mouse and human, respectively, this cross-mapping between the two ontologies facilitates the integration of mouse and human data, and the translation of basic research discoveries into clinical settings.

EVS Related References

  1. Bodenreider O, Hayamizu TF, Ringwald M, De Coronado S, Zhang S.
    Of mice and men: aligning mouse and human anatomies.
    AMIA Annu Symp Proc. 2005:61-5. PubMed PMID: 16779002; PubMed Central PMCID: PMC1560846. [PubMed]
  2. Hayamizu TF, de Coronado S, Fragoso G, Sioutos N, Kadin JA, Ringwald M.
    The mouse-human anatomy ontology mapping project.
    Database (Oxford). 2012 Mar 20;2012:bar066. Print 2012. PubMed PMID: 22434834. [PubMed] [Full Text]
  3. Kogan SC, Ward JM, Anver MR, Berman JJ, Brayton C, Cardiff RD, Carter JS, de Coronado S, Downing JR, Fredrickson TN, Haines DC, Harris AW, Harris NL, Hiai H, Jaffe ES, MacLennan IC, Pandolfi PP, Pattengale PK, Perkins AS, Simpson RM, Tuttle MS, Wong JF, Morse HC 3rd; Hematopathology subcommittee of the Mouse Models of Human Cancers Consortium.
    Bethesda proposals for classification of nonlymphoid hematopoietic neoplasms in mice.
    Blood. 2002 Jul 1;100(1):238-45. PubMed PMID: 12070033. [PubMed]

Mayo Clinic

Mayo Clinic has partnered with, supported and used EVS resources in a variety of ways for more than 10 years. This has included joint work on analyzing cancer clinical trials vocabulary and informatics needs in the U.S., improving research and clinical data representation and reuse through projects such as PHONT and   SHARPn , and development of shared community standards for vocabulary representation and infrastructure including the draft HL7/OMG standard Common Terminology Services Release 2 (CTS 2) specification and the the Mayo Clinic LexEVS terminology server that EVS has supported and uses. Mayo Clinic has a lead role in the ICD-11 revision effort, and Mayo has worked with EVS to leverage NCI Thesaurus cancer content and EVS subject matter experts in supporting revisions to the WHO ICD terminologies (see the WHO section).

Pharmacogenomics Research Network (PGRN) Ontology Network Resource (PHONT)

Mayo Clinic is the primary site for PHONT, a networked PGRN ontology resource that has been one of the highest-volume users of EVS resources. PHONT supports clear annotation and representation of phenotype (disease, adverse event, or clinical and physiological outcomes) to support data integration and cross-database analyses. PHONT has deployed its own instance of LexEVS, depending in particular on full support of CTS 2 value sets. PHONT is a collaboration with Case Western Reserve University, Harvard Medical School, MD Anderson Cancer Center, Memorial Sloan Kettering Cancer Center, University of Erlangen, and Washington University.

Strategic Health IT Advanced Research Projects: Area 4 - Secondary Use of EHR Data (SHARPn)

SHARPn Exit Disclaimer logo uses LexEVS and its value set support to help enable the reuse of EHR data for secondary purposes, such as clinical research and public health, as part of the Office of the National Coordinator for Health Information Technology SHARP Program.

EVS Related References

  1. Chute CG, Carter JS, Tuttle MS, Haber MW, Brown SH.
    Integrating pharmacokinetics knowledge into a drug ontology as an extension to support pharmacogenomics.
    AMIA Annu Symp Proc. 2003:170-4. PubMed PMID: 14728156; PubMed Central PMCID: PMC1480302. [PubMed] [Free PMC Article]
  2. Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA, Beebe CE, Huff SM.
    The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities.
    AMIA Annu Symp Proc. 2011;2011:248-56. Epub 2011 Oct 22. PubMed PMID: 22195076; PubMed Central PMCID: PMC3243296. [PubMed]
  3. Gwaltney K, Chute C, Hageman D, Kibbe W, McCormick K, Reeves D, Wright L.
    An assessment of cancer clinical trials vocabulary and IT infrastructure in the U.S.
    Proc AMIA Symp. 2001:224-8. PubMed PMID: 11825185; PubMed Central PMCID: PMC2243595. [PubMed] [Free PMC Article]
  4. Jiang G, Solbrig HR, Iberson-Hurst D, Kush RD, Chute CG.
    A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki.
    AMIA Summits Transl Sci Proc. 2010 Mar 1;2010:11-5. PubMed PMID: 21347136; PubMed Central PMCID: PMC3041544. [PubMed]
  5. Liu K, Chapman WW, Savova G, Chute CG, Sioutos N, Crowley RS.
    Effectiveness of Lexico-syntactic Pattern Matching for Ontology Enrichment with Clinical Documents.
    Methods Inf Med. 2011;50(5):397-407. Epub 2010 Nov 8. [PubMed]
  6. Pathak J, Wang J, Kashyap S, Basford M, Li R, Masys DR, Chute CG.
    Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience.
    J Am Med Inform Assoc. 2011 Jul-Aug;18(4):376-86. Epub 2011 May 19. PubMed PMID: 21597104; PubMed Central PMCID: PMC3128396. [PubMed]
  7. Zhu Q, Freimuth RR, Lian Z, Bauer S, Pathak J, Tao C, Durski MJ, Chute CG.
    Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: A case study.
    J Biomed Inform. 2013 Apr;46(2):286-93. doi: 10.1016/j.jbi.2012.11.004. Epub 2012 Nov 29. PubMed PMID: 23201637; PubMed Central PMCID: PMC3606279. [PubMed]
  8. See also: LexEVS section of Bibliography on EVS and Its Use.

MD Anderson

MD Anderson is using a wide range of EVS terminology content and technologies as part of its enterprise infrastructure. A LexEVS server and EVS terminology browsers have been deployed locally at MD Anderson. NCIt and other EVS terminology resources are also being used.

Memorial Sloan Kettering Cancer Center

The MSK Cancer Center (MSK) has used NCI Thesaurus (NCIt) in its precision oncology efforts, most notably through the OncoTree tumor type tree used in cBioPortal and its new OncoKB project.

OncoKB

OncoKB was first released in June 2016 as a precision oncology knowledge base for annotation of somatic mutations in cancer. It contains information about the effects and treatment implications of specific cancer gene alterations. It is developed and maintained by the Knowledge Systems group in the Marie Josée and Henry R. Kravis Center for Molecular Oncology (CMO), which is integrating OncoKB information with cBioPortal.

cBioPortal

The cBioPortal for Cancer Genomics provides visualization, analysis, and download of large-scale cancer genomics data sets including The Cancer Genome Atlas (TCGA).  Originally developed by, and still hosted at, MSK, cBioPortal is now developed and maintained by a multi-institutional team.

OncoTree

The OncoTree CMO Tumor Type Tree provides a user-friendly interface to 524 tumor types from 32 tissues, most linked to NCI Thesaurus (NCIt) and NCI Metathesaurus (NCIm) codes.

Ohio State University Medical Center

Ohio State is using LexEVS, NCIt, and NCIm, notably for its openMDR project. Ohio State University launched openMDR (open metadata repository) in 2009, using local instances of LexEVS, BioPortal, and caDSR.

Seoul National University, Korea

The Biomedical Knowledge Engineering (BiKE) lab adopted the 2005 version of the LexGrid model and over the last several years created an entire terminology-based application suite on that model called LexCare Suite. The VKC facilitated the signing of an agreement between Mayo Clinic and SNU's BiKE to solidify a collaboration under which they will work together on conferences and papers surrounding terminology creation/mapping/use. The BiKE mapping tool is of particular interest for a community tool in this regard. This would be a potentially significant contribution, as it would add a new tool in an area with a known gap in the current functionality of LexEVS.

Stanford University

Since 2003, EVS has worked closely with the Stanford University Center for Biomedical Informatics Research (BMIR) and the NCBO project to develop shared community tools, standards and resources. Protégé OWL and NCI Protégé have pushed the envelope on open source software for ontology development and production management. NCBO and EVS collaborated on terminology metadata standards, with caBIG and the UK National Cancer Research Institute. NCBO hosts copies of NCI Thesaurus (NCIt), one of its most highly accessed terminologies. NCIt is also used by the NCBO Annotator tool for annotating documents and data with terminology concepts. NCBO and EVS have complementary resources, and share knowledge and expertise.

Protégé

EVS has worked closely with BMIR and its predecessor, Stanford Medical Informatics. An early focus was development of Protégé OWL, so that EVS could move from proprietary to open source terminology editing software that used the emerging OWL DL standard; this was followed by support for an NCI specific plug-in that enable NCI terminology production management. Over time, many of these NCI specific changes have been rolled into the main Protégé 3.4 code, so that users worldwide could take advantage of such features as distributed collaborative terminology development. In addition, the NCI specific Protégé configuration, with its additional plug-ins, is available for non-NCI users. EVS editors and staff have been heavy users of Protégé, collaborators on collaborative ontology workflows, and testers of collaborative Protégé, Web Protégé and NCBO tools and services such as concept merging and terminology mapping tools.

National Center for Biomedical Ontology (NCBO)

NCBO Exit Disclaimer logo bases its BioPortal terminology services on LexEVS, supporting a very wide collection of some 300 terminologies and ontologies that are actively used in biomedicine. BioPortal hosts copies of NCIt, which is generally at or near the top of the NCBO ontology use chart (see section 3 above). NCBO also makes its terminologies available through caGrid.

Terminology Metadata

NCBO and EVS have collaborated on standards for terminology metadata with caBIG participants and the UK National Cancer Research Institute (NCRI), to develop and promote standards for annotating the content, structure and use of ontologies and terminologies, based on earlier work of Stanford and a European group on the Ontology Metadata Vocabulary (OMV). This work has fed into the terminology metadata content now embedded in the CTS 2 (Common Terminology Services 2) standard published by HL7 and the OMG (Object Management Group).

Tissue Microarray Database (TMAD)

TMAD Exit Disclaimer logo is an important public resource for raw and processed data (with stained tissue images) from tissue microarray experiments. TMAD uses NCI Thesaurus to index, browse and search tissues, and provides methods for data retrieval, grouping of data, analysis and visualization as well as export to standard formats.

EVS Related References

  1. Bail S, Horridge M, Parsia B, Sattler U.
    The justificatory structure of the NCBO bioportal ontologies.
    The Semantic Web–ISWC 2011. Springer 2011 pp.67-82. [PDF Exit Disclaimer logo ]
  2. Falconer SM, Tudorache T, Noy NF.
    An Analysis of Collaborative Patterns in Large-Scale Ontology Development Projects.
    Proceedings of the sixth international conference on Knowledge capture (K-CAP '11). 2011 ACM, New York, NY, USA. [ACM Exit Disclaimer logo ] [Stanford Exit Disclaimer logo ]
  3. Ghazvinian A, Noy NF, Musen MA.
    From mappings to modules: Using mappings to identify domain-specific modules in large ontologies.
    KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference, 2011, pp. 33-40. [Scopus Exit Disclaimer logo ]
  4. Jonquet C, LePendu P, Falconer S, Coulet A, Noy NF, Musen MA, Shah NH
    NCBO Resource Index: Ontology-based search and mining of biomedical resources
    Web Semantics: Science, Services and Agents on the World Wide Web, Volume 9, Issue 3, September 2011, Pages 316-324, ISSN 1570-8268, 10.1016/j.websem.2011.06.005. [ScienceDirect Exit Disclaimer logo ]
  5. Jonquet C, Musen MA, Shah NH.
    Building a biomedical ontology recommender web service.
    J Biomed Semantics. 2010 Jun 22;1 Suppl 1:S1. PubMed PMID: 20626921; PubMed Central PMCID: PMC2903720. [PubMed] [PDF Exit Disclaimer logo ]
  6. Jonquet C, Shah NH, Musen MA.
    Prototyping a Biomedical Ontology Recommender Service.
    Bio-Ontologies: Knowledge in Biology, Stockholm, Sweden (2009).
  7. Jonquet C, Shah NH, Musen MA.
    The open biomedical annotator.
    Summit on Translat Bioinforma. 2009 Mar 1;2009:56-60. PubMed PMID: 21347171; PubMed Central PMCID: PMC3041576. [PubMed] [Full Text]
  8. Noy NF, Alexander PR, Harpaz R, Whetzel PL, Fergerson RW, Musen MA.
    Getting Lucky in Ontology Search: A Data-Driven Evaluation Framework for Ontology Ranking.
    Proceedings of the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, 21-25 Oct 2013. [PDF]
  9. Parai GK, Jonquet C, Xu R, Musen MA, Shah NH.
    The Lexicon Builder Web service: Building Custom Lexicons from two hundred Biomedical Ontologies.
    AMIA Annu Symp Proc. 2010 Nov 13;2010:587-91. PubMed PMID: 21347046; PubMed Central PMCID: PMC3041331. [PubMed] [PDF]
  10. Rubin DL, Shah NH, Noy NF.
    Biomedical ontologies: a functional perspective.
    Brief Bioinform. 2008 Jan;9(1):75-90. Epub 2007 Dec 12. Review. PubMed PMID: 18077472. [PubMed]
  11. Salvadores M, Alexander PR, Musen MA, Noy NF.
    BioPortal as a Dataset of Linked Biomedical Ontologies and Terminologies in RDF.
    Semantic Web Journal 2013; 4(3):277-284. Available online 31 Oct 2012. [PDF Exit Disclaimer logo ] [Online Exit Disclaimer logo ]
  12. Salvadores M, Alexander PR, Musen MA, Noy NF.
    The Quad Economy of a Semantic Web Ontology Repository.
    The 7th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2011). pp.14-29. [PDF Exit Disclaimer logo ]
  13. Sebastian A, Noy NF, Tudorache T, Musen MA.
    A generic ontology for collaborative ontology-development workflows.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5268 LNAI, 2008, pp. 318-328. [Scopus Exit Disclaimer logo ]
  14. Shah NH, Bhatia N, Jonquet C, Rubin D, Chiang AP, Musen MA.
    Comparison of concept recognizers for building the Open Biomedical Annotator.
    BMC Bioinformatics. 2009 Sep 17;10 Suppl 9:S14. PubMed PMID: 19761568; PubMed Central PMCID: PMC2745685. [PubMed] [Full Text]
  15. Shah NH, Jonquet C, Chiang AP, Butte AJ, Chen R, Musen MA.
    Ontology-driven indexing of public datasets for translational bioinformatics.
    BMC Bioinformatics. 2009 Feb 5;10 Suppl 2:S1. PubMed PMID: 19208184; PubMed Central PMCID: PMC2646250. [PubMed]
  16. Shah NH, Musen MA.
    UMLS-Query: a perl module for querying the UMLS.
    AMIA Annu Symp Proc. 2008 Nov 6:652-6. PubMed PMID: 18998805; PubMed Central PMCID: PMC2656020. [PubMed] [Full Text]
  17. Shah NH, Rubin DL, Espinosa I, Montgomery K, Musen MA.
    Annotation and query of tissue microarray data using the NCI Thesaurus.
    BMC Bioinformatics. 2007 Aug 8;8:296. PubMed PMID: 17686183; PubMed Central PMCID: PMC1988837. [PubMed]
  18. Strohmaier M, Walk S, Pöschko J, Lamprecht D, Tudorache T, Nyulas C, Musen MA, Noy NF.
    How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects.
    Web Semantics: Science, Services and Agents on the World Wide Web, North America, April 2013. [PDF Exit Disclaimer logo ]
  19. Wu ST, Liu H, Li D, Tao C, Musen MA, Chute CG, Shah NH.
    Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis.
    J Am Med Inform Assoc. 2012 Apr 4. [Epub ahead of print] PubMed PMID: 22493050. [PubMed]

Swiss Institute of Bioinformatics (SIB)

The SIB Computer Analysis and Laboratory Investigation of Proteins of Human Origin (CALIPHO) group develops a number of biocurated resources including neXtProt and the Cellosaurus. Both resources make use of the NCI Thesaurus (NCIt).

neXtProt

neXtProt maps Catalogue of Somatic Mutations in Cancer (COSMIC) cancer terms to the corresponding terms in NCIt so as to present to its users a standardized vocabulary for the provenance of cancer protein variations extracted from COSMIC.

Cellosaurus

The Cellosaurus, a cell line thesaurus, uses NCIt to annotate cell lines originating from diseased patients and animals (mainly cancers of human and animal model origins and genetic diseases); as of March 2016, Cellosaurus contains over 25,000 cell lines linking to over 1,000 diseases in NCIt.

EVS Related References

  1. Gaudet P, Michel PA, Zahn-Zabal M, Cusin I, Duek PD, Evalet O, Gateau A, Gleizes A, Pereira M, Teixeira D, Zhang Y, Lane L, Bairoch A.
    The neXtProt knowledgebase on human proteins: current status.
    Nucleic Acids Res. 2015 Jan 28;43(Database issue):D764-70. doi: 10.1093/nar/gku1178. PubMed PMID: 25593349. [PubMed]
  2. Mottin L, Gobeill J, Pasche E, Michel PA, Cusin I, Gaudet P, Ruch P.
    neXtA5: accelerating annotation of articles via automated approaches in neXtProt.
    Database (Oxford). 2016 Jul 3;2016. pii: baw098. doi: 10.1093/database/baw098. PubMed PMID: 27374119; PubMed Central PMCID: PMC4930835. [PubMed]

University of Pittsburgh

EVS resources, notably NCI Thesaurus (NCIt) and NCI Metathesaurus (NCIm), are now deeply rooted in University of Pittsburgh core informatics applications used by hundreds of basic, translational and clinical researchers, and by many more using those applications at other institutions. University of Pittsburgh uses the NCIt cancer, anatomy, and pathology findings terminologies for their research and informatics projects. Millions of pathology reports have been encoded using NCIm and indexed using NCIt. Collaborations are ongoing, and have resulted in several journal articles published over the last 8 years.

Ontology Development Information Extraction (ODIE)

ODIE Exit Disclaimer logo is a software toolkit that uses ontologies, including NCIt, to perform information extraction tasks from clinical documents and uses clinical documents to enhance existing ontologies. ODIE is used by the University of Pittsburgh, University of California, San Diego, and Children's Hospital, Boston.

Multiple informatics projects at Pittsburgh build on the ODIE framework.

Text Information Extraction System (TIES)

TIES Exit Disclaimer logo is an end-to-end Natural Language Processing pipeline and clinical document search engine, which started as caTIES with a focus on encoding pathology reports to support access to human tissues. TIES uses NCIm as the source of concepts for named entity recognition. It also relies on NCIt for ontology-based inference, indexing and retrieval. TIES is used by multiple investigators and deployed at cancer centers and other organizations including Roswell Park, University of Arkansas, and University of Pennsylvania. At University of Pittsburgh, the system currently supports more than 200 researchers.

EVS Related References

  1. Amin W, Kang HP, Becich MJ.
    Data Management, Databases, and Warehousing.
    Biomedical Informatics for Cancer Research. (M.F. Ochs, J.T. Casagrande, R.V. Davuluri, eds). DOI 10.1007/978-1-4419-5714-6_3, © Springer Science+Business Media, LLC 2010. pp.39-71.
  2. Crowley RS, Castine M, Mitchell K, Chavan G, McSherry T, Feldman M.
    caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.
    J Am Med Inform Assoc. 2010 May-Jun;17(3):253-64. PubMed PMID: 20442142; PubMed Central PMCID: PMC2995710. [PubMed] [Full Text Exit Disclaimer logo ]
  3. Crowley RS, Tseytlin E, Jukic D.
    ReportTutor - an intelligent tutoring system that uses a natural language interface.
    AMIA Annu Symp Proc. 2005:171-5. PubMed PMID: 16779024; PubMed Central PMCID: PMC1560511. [PubMed]
  4. Jacobson RS, Becich MJ, Bollag RJ, Chavan G, Corrigan J, Dhir R, Feldman MD, Gaudioso C, Legowski E, Maihle NJ, Mitchell K, Murphy M, Sakthivel M, Tseytlin E, Weaver J.
    A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.
    Cancer Res. 2015 Dec 15;75(24):5194-201. doi:10.1158/0008-5472.CAN-15-1973. Review. PubMed PMID: 26670560; PubMed Central PMCID: PMC4683415.[PubMed]
  5. Kang HP, Borromeo CD, Berman JJ, Becich MJ.
    The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data.
    J Pathol Inform. 2010 Jul 13;1. pii: 9. PubMed PMID: 20805954; PubMed Central PMCID: PMC2929536. [PubMed]
  6. Liu K, Chapman WW, Savova G, Chute CG, Sioutos N, Crowley RS.
    Effectiveness of Lexico-syntactic Pattern Matching for Ontology Enrichment with Clinical Documents.
    Methods Inf Med. 2011;50(5):397-407. Epub 2010 Nov 8. [PubMed]
  7. Tobias J, Chilukuri R, Komatsoulis GA, Mohanty S, Sioutos N, Warzel DB, Wright LW, Crowley RS.
    The CAP Cancer Protocols – A Case Study of caCORE Based Data Standards Implementation to Integrate with the Cancer Biomedical Informatics Grid.
    BMC Medical Informatics Decision Making, 20; 6:25, 2006. [Free PMC Article]

Washington University

Washington University uses LexEVS and EVS terminology content in its clinical data warehouse project (CIDER). Washington University deployed LexEVS in 2008 as the terminology server for CIDER. The terminologies are used to code data and for information retrieval, supporting both research and clinical enterprise infrastructure.

Yale University

Yale University has used EVS resources in several biomedical research and informatics projects, including some with a special focus on semantic web technologies.

EVS Related References

  1. McCusker JP, Phillips JA, González Beltrán A, Finkelstein A, Krauthammer M.
    Semantic web data warehousing for caGrid.
    BMC Bioinformatics. 2009 Oct 1;10 Suppl 10:S2. PubMed PMID: 19796399; PubMed Central PMCID: PMC2755823. [PubMed]
  2. Shifman MA, Li Y, Colangelo CM, Stone KL, Wu TL, Cheung KH, Miller PL, Williams KR.
    YPED: a web-accessible database system for protein expression analysis.
    J Proteome Res. 2007 Oct;6(10):4019-24. Epub 2007 Sep 15. PubMed PMID: 17867667. [PubMed]

Project and Topical

Animal Models and Mappings

EVS has worked since 1999 on a variety of animal models of cancer and terminology mappings between human and non-human species. In addition to work described earlier involving the NCI Division of Cancer Biology with extensive community outreach (see the detailed profile), and the section above on work with the Jackson Laboratory, EVS has been involved in a number of other community efforts.

EVS has extended animal model terminology support to cover rats and zebrafish using two important community standards:

  • International Harmonization of Rat Nomenclature (RENI) was used as the foundation for the Terminology of Rat Pathologic Diagnoses in NCI Thesaurus (NCIt).
  • Zebrafish Information Network (ZFIN) zebrafish anatomy is provided as a standalone terminology in EVS systems including the NCI Term Browser.

EVS terminology is also being used in community efforts such as those below.

Uberon

Uberon Exit Disclaimer logo is an integrated cross-species anatomy ontology constructed using a combination of semi-automated methods and manual curation. The ontology consists of classes representing anatomical entities that are shared across a variety of metazoan organisms, with a heavy bias towards model organisms and human anatomy. Uberon contains extensive cross-references between its terms and other anatomy ontologies, and draws heavily on NCI Thesaurus (NCIt) as well as the Adult Mouse Anatomy (MA) ontology (part of the caBIG-funded Mouse-Human Anatomy Project, MHAP).

Ontology Alignment Evaluation Initiative (OAEI)

The OAEI Exit Disclaimer logo is a collaborative effort in the ontology alignment community aimed at rigorous and extensive evaluation of ontology alignment technologies. Since 2007, the OAEI has used the mouse-human anatomy set, with some modifications, as a “gold standard mapping” example of a “real world case” in an annual competitive evaluation of ontology matching approaches.

Common Biorepository Model (CBM)

EVS has provided about 1,300 concepts for this model through support for caDSR. CBM is used in many domains including clinical trials management, ICR, in vivo imaging, and tissue banks and pathology tools. In early 2012, EVS provided support for mapping SNOMED concepts used in caTissue with NCIt concepts used in CBM, to facilitate data sharing.

eMERGE Network

The eMERGE (electronic MEdical Records and GEnomics) Network Exit Disclaimer logo was initiated by National Human Genome Research Institute (NHGRI) in 2007 as a national research consortium to develop, disseminate, and apply research methods that combine DNA biorepositories with electronic medical record (EMR) systems for large-scale, high-throughput genetic research. EVS terminology plays an important role these efforts, notably through use in the eleMAP tool Exit Disclaimer logo developed at Vanderbilt to help researchers harmonize their local phenotype data dictionaries to existing metadata and terminology standards.

EVS Related References

  1. Pathak J, Wang J, Kashyap S, Basford M, Li R, Masys DR, Chute CG.
    Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience.
    J Am Med Inform Assoc. 2011 Jul-Aug;18(4):376-86. Epub 2011 May 19. PubMed PMID: 21597104; PubMed Central PMCID: PMC3128396. [PubMed]

eTOX

eTOX Exit Disclaimer logo is a European consortium established in 2010 by universities, pharmaceutical and biotech companies to share and use toxicology data. It is a pre-competitive collaboration whose main goal is to create and distribute tools to predict drug side-effects based on pre-clinical experiments. Aims are a better in silico predictability of potential adverse events and reducing the use of animals in toxicological research. The NCI Thesaurus is one of several vocabularies/ontologies used in eTOX to support shared semantics, principally the terms associated with entities as well as the relations between terms (see BioPortal). eTOX is funded by the Innovative Medicines Initiative (IMI), whose web site provides additional information (see IMI eTox Exit Disclaimer logo ).

Global Alignment of Immunization safety Assessment in pregnancy (GAIA)

The Global Alignment of Immunization safety Assessment in pregnancy (GAIA) is a global consortium to develop common standards, guidance, and tools to strengthen programs of immunization in pregnancy, with a specific focus on low and middle income countries. GAIA seeks to improve data and understanding on maternal, pregnancy, fetal, and neonatal health outcome assessment.

The National Institute of Child Health and Human Development (NICHD) and EVS have been collaborating with GAIA since 2014 to help develop standard terminology related to Fetal and Neonatal Events, Maternal, and Pregnancy Events and Outcomes. This expands on the Pediatric Terminology joint effort, ongoing since 2009, to establish a core library of harmonized pediatric terms that enable clinical investigators to more readily compare and aggregate data across clinical research portfolios. To this end, terminology data and draft standards were developed as part of the NCI Thesaurus (NCIt) by experts from the NICHD, EVS, and other participants. All NCIt content is freely available without restriction.

EVS Related References

  1. Bonhoeffer J, Kochhar S, Hirschfeld S, Heath PT, Jones CE, Bauwens J, Honrado Á, Heininger U, Muñoz FM, Eckert L, Steinhoff M, Black S, Padula M, Sturkenboom M, Buttery J, Pless R, Zuber P; GAIA project participants.
    Global alignment of immunization safety assessment in pregnancy - The GAIA project.
    Vaccine. 2016 Dec 1;34(49):5993-5997. doi: 10.1016/j.vaccine.2016.07.006. PubMed PMID: 27751641. [PubMed]

Human Studies Database (HSDB)

HSDB Exit Disclaimer logo is a consortium of research institutions defining and implementing a shared informatics infrastructure covering both interventional and observational human studies. HSDB is using LexEVS as a core component of its collaborative, distributed, clinical research systems. The OCRe Terminology Exit Disclaimer logo is served through an NCBO site, which uses LexEVS.

EVS Related References

  1. Sim I, Carini S, Tu S, Wynden R, Pollock BH, Mollah SA, Gabriel D, Hagler HK, Scheuermann RH, Lehmann HP, Wittkowski KM, Nahm M, Bakken S.
    The Human Studies Database Project: Federating Human Studies Design Data Using the Ontology of Clinical Research.
    AMIA Summits Transl Sci Proc. 2010: 51–55. [Online]

International Human Epigenome Consortium (IHEC)

IHEC Exit Disclaimer logo started in 2010 as a global consortium with the primary goal of providing free access to high-resolution reference human epigenome maps for normal and disease cell types to the research community. IHEC coordinates the production of reference epigenome maps through the characterization of the regulome, methylome, and transcriptome from a wide range of tissues and cell types. IHEC chose NCIt as the ontology to represent disease information within the Consortium, which also uses NCI Metathesaurus as a key resource.

EVS Related References

  1. Bujold D, Morais DA, Gauthier C, Côté C, Caron M, Kwan T, Chen KC, Laperle J, Markovits AN, Pastinen T, Caron B, Veilleux A, Jacques PÉ, Bourque G.
    The International Human Epigenome Consortium Data Portal.
    Cell Syst. 2016 Nov 23;3(5):496-499.e2. doi: 10.1016/j.cels.2016.10.019. PubMed PMID: 27863956. [PubMed]
  2. Dyke SO, Cheung WA, Joly Y, Ammerpohl O, Lutsik P, Rothstein MA, Caron M, Busche S, Bourque G, Rönnblom L, Flicek P, Beck S, Hirst M, Stunnenberg H, Siebert R, Walter J, Pastinen T.
    Epigenome data release: a participant-centered approach to privacy protection.
    Genome Biol. 2015 Jul 17;16(1):142. PubMed PMID: 26185018; PubMed Central PMCID: PMC4504083. [PubMed]
  3. Fernández JM, de la Torre V, Richardson D, Royo R, Puiggròs M, Moncunill V, Fragkogianni S, Clarke L; BLUEPRINT Consortium., Flicek P, Rico D, Torrents D, Carrillo de Santa Pau E, Valencia A.
    The BLUEPRINT Data Analysis Portal.
    Cell Syst. 2016 Nov 23;3(5):491-495.e5. doi: 10.1016/j.cels.2016.10.021. PubMed PMID: 27863955. [PubMed]

Imaging Standards

Imaging standards development requires the support of multiple products made available through EVS, including publication of RadLex terminology, developed through a collaboration of the Radiological Society of North America (RSNA), which convened experts in imaging informatics and radiological subspecialties to create this resource, now made freely available. RadLex has developed into a rich, structured radiology-specific ontology, which currently includes more than 30,000 terms and to which EVS provides content as well as publication support. EVS incorporates RadLex into the NCI Metathesaurus, and also supports imaging terminology in the NCI Thesaurus as needed by the imaging community.

EVS Related References

  1. Hazen R, Van Esbroeck AP, Mongkolwat P, Channin DS.
    Automatic Extraction of Concepts to Extend RadLex.
    Journal of Digital Imaging 2011 Feb;24(1):165-169. [PDF]
  2. Lacson R, Andriole KP, Prevedello LM, Khorasani R.
    Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT).
    Journal of Digital Imaging 2012 Feb 14:1-8 [Epub ahead of print] PubMed PMID: 22349993. [PubMed] [Online]
  3. Warden GI, Lacson R, Khorasani R.
    Leveraging Terminologies for Retrieval of Radiology Reports with Critical Imaging Findings.
    AMIA Annu Symp Proc. 2011 Oct;2011:1481-8. Epub 2011 Oct 22. PubMed PMID: 22195212; PubMed Central PMCID: PMC3243125. [PubMed]

LexEVS Adopter Community

Many other organizations have adopted the LexEVS terminology server and related tools. Key examples described in the institutional portion of this section are:

  1. Emory University
  2. GE
  3. MD Anderson
  4. Mayo Clinic (incl. PHONT and SHARPn)
  5. McGill University Health Center, Canada
  6. Ohio State University Medical Center
  7. Seoul National University, Korea
  8. Stanford/National Center for Biomedical Ontology (NCBO)
  9. Washington University

For literature references, see the LexEVS section of Bibliography on EVS and Its Use.

Nanotechnology

Nanotechnology, and more specifically nanomedicine, has become important in the development of reagents for cancer detection, diagnosis and treatment. NCI established Cancer Centers of Nanotechnology Excellence (CCNE) to support translational nanomedical research, and collaborative efforts are supported by NHLBI, NHGRI, FDA and others. EVS provides a range of content and technical support to the nanotechnology community, including working with the community in curating specialized concepts and definitions, and making those concepts available in NCI Thesaurus (NCIt) and the NanoParticle Ontology (NPO) hosted on EVS systems and integrated into NCI Metathesaurus (NCIm); a glossary of nanotechnology terms also is provided within the caNanoLab application (see below).

Nanotechnology Working Group

The NCIP Nanotechnology Working Group was started as part of the caBIG® Integrative Cancer Research Workspace (ICR Nano WG), with participation of approximately 40 agencies, universities and institutes working to federate nanotechnology databases. One requirement has been to develop data and vocabulary standards to facilitate federation and increase data accessibility. EVS has been an active participant in the working group. Part of the working group effort is the continuing development of the NanoParticle Ontology (NPO), from Washington University in St. Louis, and the development of the ISA-TAB-Nano data sharing format. EVS has supported this effort in several areas: giving feedback on ontology structure and terminology best practices, supporting NPO curation using the NCI Protégé platform, loading and hosting NPO on LexEVS, and preparing NPO for integration into the NCI Metathesaurus (NCIm).

caNanoLab

The NCI Office of Cancer Nanotechnology Research (OCNR) partnered with CBIIT and the NCI Nanotechnology Characterization Laboratory (NCL) in 2006 to develop a data sharing platform called caNanoLab. caNanoLab has a goal of semantic interoperability across centers performing nanoparticle characterization studies. caNanoLab is based on an information model representing nanoparticles and their physical and in vitro characterization. NCI Thesaurus (NCIt) has been supporting concept curation for development of caNanoLab since its inception. NCIt editors have worked with the developers and users of caNanoLab from the CCNEs and other academic institutions to define concepts for data curation and to expand the object model to include data submission for both characterizations of experimental nanomaterials and translational research studies.

The infrastructure available for further development of caNanoLab has been decreased over the last several years, but caNanoLab continues to be maintained and is heavily used. As of February 21, 2014, it contains 1,027 samples, 95 sample sources, 4,025 characterizations, and 46 protocols, and identifies 1,894 publications, while the caNanoLab home page shows 420,937 visitors since June 3, 2010. For more information about caNanoLab, visit the wiki home page.

ISA-TAB-Nano

ISA-TAB-Nano is being developed by a subset of participants of the NCIP Nanotechnology Working Group, which includes members representing CBIIT, Oregon State University, PNNL, Washington University St. Louis, Stanford, Jackson Labs, Pennsylvania BioNano Systems, NIOSH, NCI Frederick NCL, and Emory/Georgia Tech. ISA-TAB-Nano is a data representation format that is designed to facilitate sharing research related to the in vivo and in vitro characterization of nanomaterials and any associated small molecules or biological specimens. This format is compatible with spreadsheets or tab-delimited files. ISA-TAB-Nano is based on the existing specification developed by the ISA community, the investigation/study/assay (ISA-TAB) format specification. ISA-TAB was designed to assist in recording and sharing of both data and metadata associated with the large volume of data generated by the numerous assays and technology types used in the “omics” communities.

The ISA-TAB file structure relies on three primary files: investigation, study, and assay (ISA) files. The ISA-TAB-Nano specification adds additional fields to the ISA-TAB files and an additional material file to record nanomaterial and small molecule structural and functional characteristics. ISA-TAB-Nano has been reviewed as an ASTM standard (WK28974). Further development for this data sharing format is focused on training users, improving the usability of the format and increasing the compatibility between ISA-TAB-Nano and the ISA community curation and validation tool sets. See the ISA-TAB-Nano Specification for more information.

NanoParticle Ontology (NPO)

The NanoParticle Ontology (NPO Exit Disclaimer logo ) is loaded in NCI Metathesaurus, on LexEVS, and on the NCI Term Browser. NPO curators use the NCI Protégé curation tool and they use NCI Thesaurus as a cross-reference and a source for some of their definitions.

EVS Related References

  1. Bailey LO, Kennedy CH, Fritts MJ, Hartel FW.
    Development of a model for the representation of nanotechnology-specific terminology.
    AMIA Annu Symp Proc. 2006:849. PubMed PMID: 17238469; PubMed Central PMCID: PMC1839578. [PubMed]
  2. de la Iglesia D, Cachau RE, García-Remesal M, Maojo V.
    Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research.
    Comput Sci Discov. 2013 Nov 27;6(1):014011. PubMed PMID: 24932210; PubMed Central PMCID: PMC4053539. [PubMed]
  3. de la Iglesia D, García-Remesal M, Anguita A, Muñoz-Mármol M, Kulikowski C, Maojo V.
    A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov.
    PLoS One. 2014 Oct 27;9(10):e110331. doi: 10.1371/journal.pone.0110331. eCollection 2014. PubMed PMID: 25347075; PubMed Central PMCID: PMC4210133. [PubMed Exit Disclaimer logo ]
  4. Klaessig FG.
    Developing official practices for nanoEHS data compilation, curation and compliance.
    Innovation and responsibility: engaging with new and emerging technologies, S.NET 005. Coenen C, Dijkstra A, Fautz C, Guivant J, Konrad K, Milburn C, van Lente H, eds. Heidelberg: IOS Press and AKA; 2014. pp. 121–133. [PDF Exit Disclaimer logo ]
  5. Thomas DG, Gaheen S, Harper SL, Fritts M, Klaessig F, Hahn-Dantona E, Paik D, Pan S, Stafford GA, Freund ET, Klemm JD, Baker NA.
    ISA-TAB-Nano: a specification for sharing nanomaterial research data in spreadsheet-based format.
    BMC Biotechnol. 2013 Jan 14;13:2. doi: 10.1186/1472-6750-13-2. PubMed PMID: 23311978;
    PubMed Central PMCID: PMC3598649. [PubMed]
  6. Thomas DG, Klaessig F, Harper SL, Fritts M, Hoover MD, Gaheen S, Stokes TH, Reznik-Zellen R, Freund ET, Klemm JD, Paik DS, Baker NA.
    Informatics and standards for nanomedicine technology.
    Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2011 Jun 30. doi: 10.1002/wnan.152. Epub ahead of print. PubMed PMID: 21721140. [PubMed]
  7. Thomas DG, Pappu RV, Baker NA.
    NanoParticle Ontology for cancer nanotechnology research.
    J Biomed Inform. 2011 Feb;44(1):59-74. Epub 2010 Mar 6. PubMed PMID: 20211274; PubMed Central PMCID: PMC3042056. [PubMed]
  8. Zhu Z.
    Flash Nanoprecipitation: Prediction and Enhancement of Particle Stability via Drug Structure.
    Mol Pharm. 2014 Feb 3. [Epub ahead of print] PubMed PMID: 24484077. [PubMed]

Open Biomedical Ontologies (OBO)

EVS has worked with the OBO Foundry group since around 2005 to develop shared principles for open ontologies. NCI Thesaurus is designated as an Application ontology, since it uses and references domain ontologies within the OBO Foundry group. EVS makes several of the OBO Foundry ontologies available through LexEVS for community use. (For more information, visit the OBO Foundry website Exit Disclaimer logo .)

EVS Related References

  1. De Coronado S, Tuttle MS, Solbrig HR.
    Using the UMLS Semantic Network to Validate NCI Thesaurus Structure and Analyze its Alignment with the OBO Relations Ontology.
    AMIA Annu Symp Proc. 2007:165-70. [NCBI]

PhenX

PhenX Exit Disclaimer logo was initiated by National Human Genome Research Institute (NHGRI) in 2007 to develop consensus measures for phenotypes and exposures in support of genome-wide association studies (GWAS) and other large-scale research efforts. Run by RTI International with broad participation among NIH institutes and the research community, PhenX initially prioritized 21 research domains relevant to genomics research and public health. EVS provides ongoing terminology support for these domains and related PhenX efforts.

Translational Research and Patient Safety in Europe (TRANSFoRm)

The TRANSFoRm Exit Disclaimer logo 2010-2015 European Commission project for European health care reform involved a consortium of 15 European Universities and two private partners, led by King’s College London, to develop methods, standards and systems for the integration of healthcare computer systems for clinical care and research. EVS and the VKC supported TRANSFoRm terminology efforts. The TRANSFoRm Integrated Vocabulary Service (TRANSFoRm VS) used the LexEVS terminology server and other EVS resources; the VKC worked with TRANSFoRm to develop a new loader for the WHO Anatomical Therapeutic Chemical (ATC) terminology, including requirements gathering, coding, testing, and documentation; and efforts to integrate with and extend the NIH-funded electronic Primary Care Research Network (ePCRN) and ePCRN Workbench have used both LexEVS and the NCI Metathesaurus.

EVS Related References

  1. Delaney BC, Curcin V, Andreasson A, Arvanitis TN, Bastiaens H, Corrigan D, Ethier JF, Kostopoulou O, Kuchinke W, McGilchrist M, van Royen P, Wagner P.
    Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe.
    Biomed Res Int. 2015;2015:961526. doi: 10.1155/2015/961526. Epub 2015 Oct 11. PubMed PMID: 26539547; PubMed Central PMCID: PMC4619923. [PubMed]
  2. Ethier JF, Curcin V, Barton A, McGilchrist MM, Bastiaens H, Andreasson A, Rossiter J, Zhao L, Arvanitis TN, Taweel A, Delaney BC, Burgun A.
    Clinical data integration model. Core interoperability ontology for research using primary care data.
    Methods Inf Med. 2015;54(1):16-23. doi: 10.3414/ME13-02-0024. Epub 2014 Jun 18. PubMed PMID: 24954896. [PubMed]
  3. Ethier JF, Dameron O, Curcin V, McGilchrist MM, Verheij RA, Arvanitis TN, Taweel A, Delaney BC, Burgun A.
    A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.
    J Am Med Inform Assoc. 2013 Sep 1;20(5):986-94. doi: 10.1136/amiajnl-2012-001312. Epub 2013 Apr 9. [PubMed]

Appendix 1 - EVS Server Statistics Sources

This section identifies links to the various statistical display pages for each of the EVS browsers and APIs.

NCI Thesaurus Browser (ncit.nci.nih.gov)

https://nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=ncit
https://ncicbwebstats.nci.nih.gov/webstats/ncitrep

NCI Metathesaurus Browser (ncim.nci.nih.gov)

https://nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=ncim
https://ncicbwebstats.nci.nih.gov/webstats/ncimrep

NCI Terms Browser (nciterms.nci.nih.gov)

https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=nciterms
https: //ncicbwebstats.nci.nih.gov/webstats/ncitermsrep

NCI Term Suggestion Form (ncitermform.nci.nih.gov)

https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=ncitermform 
https: //ncicbwebstats.nci.nih.gov/webstats/ncitermform 

LexEVS API 4.2 and 5.0 (lexevsapi.nci.nih.gov/lexevsapi42 and /lexevsapi50)

https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi 
https: //ncicbwebstats.nci.nih.gov/webstats/lexevsapirep 
https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi-analytical50 
https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi-data50

LexEVS API 5.1 (lexevsapi51.nci.nih.gov)

https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi51 
https: //ncicbwebstats.nci.nih.gov/webstats/lexevsapi51rep 
https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi-analytical51 
https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi-data51

LexEVS API 6.0 (lexevsapi60.nci.nih.gov)

https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi60 
https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi-analytical60 
https: //nci6116-aws03.nci.nih.gov/awstats/awstats.pl?config=lexevsapi-data60

EVS Website (evs.nci.nih.gov)

http: //ncias-p641-v.nci.nih.gov/awstats/awstats.pl?config=evs.nci.nih.gov

Bibliography on EVS and Its Use

This bibliography includes journal articles and other scientific literature about EVS and the explicit use of EVS resources in support of cancer research and related scientific efforts. It does not include materials where EVS resources play an important role but are not explicitly addressed (as often occurs, for example, when use is made of NCI's semantic infrastructure and tooling), or of EVS supported standards maintained jointly with other partners such as CTCAE, FDA SPL and ICSR, and CDISC SDTM.

About EVS

  1. He Z, Chen Y, de Coronado S, Piskorski K, Geller J.
    Topological-Pattern-Based Recommendation of UMLS Concepts for National Cancer Institute Thesaurus.
    AMIA Annu Symp Proc. 2017 Feb 10;2016:618-627. PubMed PMID: 28269858. [PubMed]

    2013
  2. Copeland M, Gonçalves RS, Parsia B, Sattler U, Stevens R.
    Finding fault: Detecting issues in a versioned ontology.
    Proc Second International Workshop on Debugging Ontologies and Ontology Mappings (WoDOOM 2013) May 27, 2013, part of the European Semantic Web Symposium (ESWS 2013), Montpellier, France. [PDF Exit Disclaimer logo ]
     
    2012
  3. Elkin PL, Tuttle MS, Rallins M, Trajkovski J, Lumakovska E, Brown SH.
    Implementations of Terminology.
    In Elkin PL, ed. Terminology and Terminological Systems, Ch.8 pp.125-175, Springer London, 2012. [Springer Exit Disclaimer logo ]
     
    2011
  4. Gonçalves RS, Parsia B, Sattler U.
    Analysing the evolution of the NCI thesaurus.
    Proc IEEE Symposium on Computer-Based Medical Systems (CBMS-11) Bristol, United Kingdom, June 27-30, 2011. [At IEEE Xplore Exit Disclaimer logo ]
  5. Gonçalves RS, Parsia B, Sattler U.
    Analysing Multiple Versions of an Ontology: A Study of the NCI Thesaurus.
    Proc 24th International Workshop on Description Logics (DL 2011), Barcelona, Spain, July 13-16, 2011. [PDF Exit Disclaimer logo ]
     
    2009
  6. De Coronado S, Wright LW, Fragoso G, Haber MW, Hahn-Dantona EA, Hartel FW, Quan SL, Safran T, Thomas N, Whiteman L.
    The NCI Thesaurus Quality Assurance Life Cycle.
    Journal of Biomedical Informatics 2009 June;42(3):530-539. [PubMed]
     
    2008
  7. Noy NF, de Coronado S, Solbrig H, Fragoso G, Hartel FW, Musen MA.
    Representing the NCI Thesaurus in OWL DL: Modeling tools help modeling languages.
    Appl Ontol. 2008 Jan 1;3(3):173-190. [PubMed] [Free PMC Article]
     
    2007
  8. De Coronado S, Tuttle MS, Solbrig HR.
    Using the UMLS Semantic Network to Validate NCI Thesaurus Structure and Analyze its Alignment with the OBO Relations Ontology.
    AMIA Annu Symp Proc. 2007:165-70. [PubMed] [Available online]
  9. Haber MW, Kisler BW, Lenzen M, Wright LW.
    Controlled Terminology for Clinical Research: A Collaboration between CDISC and NCI Enterprise Vocabulary Services.
    Drug Information Journal 2007;41(3):405-412. [PDF Exit Disclaimer logo ]
  10. Sioutos N, De Coronado S, Haber MW, Hartel FW, Shaiu WL, Wright LW.
    NCI Thesaurus: A Semantic Model Integrating Cancer-Related Clinical and Molecular Information.
    Journal of Biomedical Informatics 2007 Feb;40(1):30-43. Epub 2006 Mar 15. [PubMed]
     
    2005
  11. Hartel FW, De Coronado S, Dionne R, Fragoso G, Golbeck J.
    Modeling a description logic vocabulary for cancer research.
    Journal of Biomedical Informatics, 2005; 38(2):114-129. [PubMed]
     
    2004
  12. De Coronado S, Haber MW, Sioutos N, Tuttle MS, Wright LW.
    NCI Thesaurus: Using Science-Based Terminology to Integrate Cancer Research Results.
    Proc 11th World Congress on Medical Informatics (Medinfo 2004) Amsterdam: IOS Press 2004, pp. 33-37. Also cited as: Stud Health Technol Inform. 2004;107(Pt 1):33-7. [PubMed]
  13. Fragoso G, De Coronado S, Haber MW, Hartel FW, Wright LW.
    Overview and Utilization of the NCI Thesaurus.
    Comparative and Functional Genomics (Dec. 2004) 5(8):648-654. [PubMed] [Free PMC Article]
     
    2003
  14. Goldbeck J, Fragoso G, Hartel F, Hendler J, Oberthaler J, Parsia B.
    The National Cancer Institute's Thesaurus and Ontology.
    Journal of Web Semantics (2003) 1:75-80. [JWS Web Site Exit Disclaimer logo ]
  15. Hartel F, Fragoso G, Ong K, Dionne R.
    Enhancing Quality of Retrieval Through Concept Edit History.
    AMIA Annu Symp Proc. 2003:279-83. [PubMed]

    2002
  16. Hartel FW, de Coronado S.
    Information Standards Within the National Cancer Institute.
    Cancer Informatics: Essential Technologies for Clinical Trials JS Silva, et al., eds. Springer-Verlag; 2002 Jan 8. pp. 135-156.

    2001
  17. Gwaltney K, Chute C, Hageman D, Kibbe W, McCormick K, Reeves D, Wright L.
    An assessment of cancer clinical trials vocabulary and IT infrastructure in the U.S.
    Proc AMIA Symp. 2001:224-8. PubMed PMID: 11825185; PubMed Central PMCID: PMC2243595. [PubMed] [Free PMC Article]
  18. Hubbard SL.
    Information systems in oncology.
    Cancer: Principles & Practice of Oncology, 6th ed. Devita VT, Hellman S, Rosenberg SA, eds. Philadelphia: Lippincott Williams & Wilkins; 2001. pp. 3135–46.
  19. Hubbard SM, Setser A.
    The Cancer Informatics Infrastructure: a new initiative of the National Cancer Institute.
    Semin Oncol Nurs. 2001 Feb;17(1):55-61. Review. PubMed PMID: 11236366. [PubMed] [Full Text Exit Disclaimer logo ]
  20. Silva JS, Ball MJ, Douglas JV.
    The Cancer Informatics Infrastructure (CII): An Architecture for Translating Clinical Research into Patient Care.
    Proc 10th World Congress on Medical Informatics (Medinfo 2001) Amsterdam: IOS Press 2001, pp. 114-117. Also cited as: Stud Health Technol Inform. 2001;84(Pt 1):114-7. PubMed PMID: 11604717. [PubMed]

    1999
  21. Silva J, Wittes R.
    Role of clinical trials informatics in the NCI's cancer informatics infrastructure.
    Proc AMIA Symp. 1999:950-4. PubMed PMID: 10566501; PubMed Central PMCID: PMC2232686. [PubMed] [Free PMC Article]

Biomedical Applications

2018

  1. Po-Yen Lin F, Groza T, Kocbek S, Cancer

    Care Treatment Outcome Ontology: A Novel Computable Ontology for Profiling Treatment Outcomes in Patients With Solid Tumors

    September 2018, DOI: 10.1200/CCI.18.00026

2017

  1. Gipson DS, Kirkendall ES, Gumbs-Petty B, Quinn T, Steen A, Hicks A, McMahon A, Nicholas S, Zhao-Wong A, Taylor-Zapata P, Turner M, Herreshoff E, Jones C, Davis JM, Haber M, Hirschfeld S.
    Development of a Pediatric Adverse Events Terminology.
    Pediatrics. 2017 Jan;139(1). Available online Dec 27, 2016. pii: e20160985. doi: 10.1542/peds.2016-0985. [Epub ahead of print] PubMed PMID: 28028203. [PubMed]
  2. Jouhet V, Mougin F, Bréchat B, Thiessard F.
    Building a model for disease classification integration in oncology, an approach based on the national cancer institute thesaurus.
    J Biomed Semantics. 2017 Feb 7;8(1):6. doi: 10.1186/s13326-017-0114-4. PubMed PMID: 28173841; PubMed Central PMCID: PMC5294908. [PubMed]
  3. Noor A, Assiri A, Ayvaz S, Clark C, Dumontier M.
    Drug-drug interaction discovery and demystification using Semantic Web technologies.
    J Am Med Inform Assoc. 2017. Available online 2016 Dec 28. pii: ocw128. doi: 10.1093/jamia/ocw128. [Epub ahead of print] [PubMed]
  4. Schilsky RL.
    Finding the Evidence in Real-World Evidence: Moving from Data to Information to Knowledge.
    J Am Coll Surg. 2017 Jan;224(1):1-7. doi: 10.1016/j.jamcollsurg.2016.10.025. PubMed PMID: 27989954. [PubMed]
  5. Sharma DK, Solbrig HR, Prud'hommeaux E, Pathak J, Jiang G.
    Standardized Representation of Clinical Study Data Dictionaries with CIMI Archetypes.
    AMIA Annu Symp Proc. 2017 Feb 10;2016:1119-1128. PubMed PMID: 28269909. [PubMed]
  6. Visvanathan K, Levit LA, Raghavan D, Hudis CA, Wong S, Dueck A, Lyman GH.
    Untapped Potential of Observational Research to Inform Clinical Decision Making: American Society of Clinical Oncology Research Statement.
    J Clin Oncol. 2017 Mar 30:JCO2017726414. doi: 10.1200/JCO.2017.72.6414. [Epub ahead of print] PubMed PMID: 28358653. [PubMed]
  7. Yan Z, Lacson R, Ip I, Valtchinov V, Raja A, Osterbur D, Khorasani R.
    Evaluating Terminologies to Enable Imaging-Related Decision Rule Sharing.
    AMIA Annu Symp Proc. 2017 Feb 10;2016:2082-2089. PubMed PMID: 28269968. [PubMed]

    2016
  8. Bonhoeffer J, Kochhar S, Hirschfeld S, Heath PT, Jones CE, Bauwens J, Honrado Á, Heininger U, Muñoz FM, Eckert L, Steinhoff M, Black S, Padula M, Sturkenboom M, Buttery J, Pless R, Zuber P; GAIA project participants.
    Global alignment of immunization safety assessment in pregnancy - The GAIA project.
    Vaccine. 2016 Dec 1;34(49):5993-5997. doi: 10.1016/j.vaccine.2016.07.006. PubMed PMID: 27751641. [PubMed]
  9. Fathiamini S, Johnson AM, Zeng J, Araya A, Holla V, Bailey AM, Litzenburger BC, Sanchez NS, Khotskaya Y, Xu H, Meric-Bernstam F, Bernstam EV, Cohen T.
    Automated identification of molecular effects of drugs (AIMED).
    J Am Med Inform Assoc. 2016 Jul;23(4):758-65. doi: 10.1093/jamia/ocw030. PubMed PMID: 27107438; PubMed Central PMCID: PMC4926748. [PubMed]
  10. Fernández JM, de la Torre V, Richardson D, Royo R, Puiggròs M, Moncunill V, Fragkogianni S, Clarke L; BLUEPRINT Consortium., Flicek P, Rico D, Torrents D, Carrillo de Santa Pau E, Valencia A.
    The BLUEPRINT Data Analysis Portal.
    Cell Syst. 2016 Nov 23;3(5):491-495.e5. doi: 10.1016/j.cels.2016.10.021. PubMed PMID: 27863955. [PubMed]
  11. Forbes SA, Beare D, Boutselakis H, Bamford S, Bindal N, Tate J, Cole CG, Ward S, Dawson E, Ponting L, Stefancsik R, Harsha B, Kok CY, Jia M, Jubb H, Sondka Z, Thompson S, De T, Campbell PJ.
    COSMIC: somatic cancer genetics at high-resolution.
    Nucleic Acids Res. 2016 Nov 28. pii: gkw1121. [Epub ahead of print] PubMed PMID: 27899578. [PubMed]
  12. Hochheiser H, Castine M, Harris D, Savova G, Jacobson RS.
    An information model for computable cancer phenotypes.
    BMC Med Inform Decis Mak. 2016 Sep 15;16(1):121. doi: 10.1186/s12911-016-0358-4. PubMed PMID: 27629872. [PubMed]
  13. Huang J, Eilbeck K, Smith B, Blake JA, Dou D, Huang W, Natale DA, Ruttenberg A, Huan J, Zimmermann MT, Jiang G, Lin Y, Wu B, Strachan HJ, He Y, Zhang S, Wang X, Liu Z, Borchert GM, Tan M.
    The Non-Coding RNA Ontology (NCRO): a comprehensive resource for the unification of non-coding RNA biology.
    J Biomed Semantics. 2016 May 4;7:24. doi: 10.1186/s13326-016-0066-0. PubMed PMID: 27152146; PubMed Central PMCID: PMC4857245. [PubMed]
  14. Kaufman L, Gore K, Zandee JC.
    Data Standardization, Pharmaceutical Drug Development, and the 3Rs.
    ILAR J. 2016 Dec;57(2):109-119. doi: 10.1093/ilar/ilw030. PubMed PMID: 28053065. [PubMed]
  15. Kim E, Corte-Real M, Baloch Z.
    A Deep Semantic Mobile Application for Thyroid Cytopathology.
    SPIE Medical Imaging 2016: Advanced PACS-based Imaging Informatics and Therapeutic Applications, 2016. San Diego, California, USA, 27 Feb - 3 Mar 2016. [PubMed]
  16. Mottin L, Gobeill J, Pasche E, Michel PA, Cusin I, Gaudet P, Ruch P.
    neXtA5: accelerating annotation of articles via automated approaches in neXtProt.
    Database (Oxford). 2016 Jul 3;2016. pii: baw098. doi: 10.1093/database/baw098. PubMed PMID: 27374119; PubMed Central PMCID: PMC4930835. [PubMed]
  17. Piñero J, Bravo À, Queralt-Rosinach N, Gutiérrez-Sacristán A, Deu-Pons J, Centeno E, García-García J, Sanz F, Furlong LI.
    DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants.
    Nucleic Acids Res. 2016 Oct 19. pii: gkw943. [Epub ahead of print] PubMed PMID: 27924018. [PubMed]
  18. Ritter DI, Roychowdhury S, Roy A, Rao S, Landrum MJ, Sonkin D, Shekar M, Davis CF, Hart RK, Micheel C, Weaver M, Van Allen EM, Parsons DW, McLeod HL, Watson MS, Plon SE, Kulkarni S, Madhavan S; ClinGen Somatic Cancer Working Group.
    Somatic cancer variant curation and harmonization through consensus minimum variant level data.
    Genome Med. 2016 Nov 4;8(1):117. PubMed PMID: 27814769; PubMed Central PMCID: PMC5095986. [PubMed]
  19. Schilsky RL, Miller RS.
    Creating a Learning Health Care System in Oncology.
    Oncology Informatics: Using Health Information Technology to Improve Processes and Outcomes in Cancer. Hesse BW, Ahern DK, Beckjord E, eds. Elsevier Academic Press (2016), pp. 3–21.
  20. Souza AD, Almeida MB.
    Natural Language Definitions for the Leukemia Knowledge Domain.
    Proc International Conference on Biomedical Ontology 2016 (ICBO 2016). Corvallis, Oregon, USA, 1-4 Aug 2016. [PDF Exit Disclaimer logo ]
  21. Sward KA, Rubin S, Jenkins TL, Newth CJ, Dean JM; Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD) Collaborative Pediatric Critical Care Research Network (CPCCRN).
    Case Study: Semantic Annotation of a Pediatric Critical Care Research Study.
    Comput Inform Nurs. 2016 Mar;34(3):101-4. doi: 10.1097/CIN.0000000000000236. PubMed PMID: 26958992; PubMed Central PMCID: PMC4788017. [PubMed]
  22. Warner JL, Rioth MJ, Mandl KD, Mandel JC, Kreda DA, Kohane IS, Carbone D, Oreto R, Wang L, Zhu S, Yao H, Alterovitz G.
    SMART precision cancer medicine: a FHIR-based app to provide genomic information at the point of care.
    J Am Med Inform Assoc. 2016 Mar 27. pii: ocw015. doi: 10.1093/jamia/ocw015. [Epub ahead of print] PubMed PMID: 27018265. [PubMed]
  23. Wood F.
    The Standard for the Exchange of Nonclinical Data (SEND): History, Basics, and Comparisons with Clinical Data.
    PharmaSUG 2016 Conference Proceedings. Denver, Colorado, May 8-11, 2016. [PDF Exit Disclaimer logo ]

    2015
  24. Anzai T, Kaminishi M, Sato K, Kaufman L, Iwata H, Nakae D.
    Responses to the Standard for Exchange of Nonclinical Data (SEND) in non-US countries.
    J Toxicol Pathol. 2015 Apr;28(2):57-64. doi: 10.1293/tox.2015-0007. Epub 2015 Apr 1. Review. PubMed PMID: 26028814; PubMed Central PMCID: PMC4444503. [PubMed][PDF Exit Disclaimer logo ]
  25. Duellman SJ, Zhou W, Meisenheimer P, Vidugiris G, Cali JJ, Gautam P, Wennerberg K, Vidugiriene J.
    Bioluminescent, Nonlytic, Real-Time Cell Viability Assay and Use in Inhibitor Screening.
    Assay Drug Dev Technol. 2015 Sep 18. [Epub ahead of print] PubMed PMID: 26383544. [PubMed]
  26. Dyke SO, Cheung WA, Joly Y, Ammerpohl O, Lutsik P, Rothstein MA, Caron M, Busche S, Bourque G, Rönnblom L, Flicek P, Beck S, Hirst M, Stunnenberg H, Siebert R, Walter J, Pastinen T.
    Epigenome data release: a participant-centered approach to privacy protection.
    Genome Biol. 2015 Jul 17;16(1):142. PubMed PMID: 26185018; PubMed Central PMCID: PMC4504083. [PubMed]
  27. Fransson MN, Rial-Sebbag E, Brochhausen M, Litton JE.
    Toward a common language for biobanking.
    Eur J Hum Genet. 2015 Jan;23(1):22-8. doi: 10.1038/ejhg.2014.45. Epub 2014 Apr 9. PubMed PMID: 24713663; PubMed Central PMCID: PMC4266732. [PubMed]
  28. Fu X, Batista-Navarro R, Rak R, Ananiadou S.
    Supporting the annotation of chronic obstructive pulmonary disease (COPD) phenotypes with text mining workflows.
    J Biomed Semantics. 2015 Mar 14;6:8. doi: 10.1186/s13326-015-0004-6. eCollection 2015. PubMed PMID: 25789153; PubMed Central PMCID: PMC4364458. [PubMed]
  29. Gaudet P, Michel PA, Zahn-Zabal M, Cusin I, Duek PD, Evalet O, Gateau A, Gleizes A, Pereira M, Teixeira D, Zhang Y, Lane L, Bairoch A.
    The neXtProt knowledgebase on human proteins: current status.
    Nucleic Acids Res. 2015 Jan 28;43(Database issue):D764-70. doi: 10.1093/nar/gku1178. PubMed PMID: 25593349. [PubMed]
  30. Hicks KA, Tcheng JE, Bozkurt B, Chaitman BR, Cutlip DE, Farb A, Fonarow GC, Jacobs JP, Jaff MR, Lichtman JH, Limacher MC, Mahaffey KW, Mehran R, Nissen SE, Smith EE, Targum SL.
    2014 ACC/AHA Key Data Elements and Definitions for Cardiovascular Endpoint Events in Clinical Trials: A Report of the American College of Cardiology/American Heart Association Task Force on Clinical Data Standards (Writing Committee to Develop Cardiovascular Endpoints Data Standards).
    J Am Coll Cardiol. 2015 Jul 28;66(4):403-69. doi: 10.1016/j.jacc.2014.12.018. Epub 2014 Dec 29. PubMed PMID: 25553722. [PubMed]
    J Nucl Cardiol. 2015 Jul 24. [Epub ahead of print] PubMed PMID: 26204990. [PubMed]
  31. Hostenkamp G, Lichtenberg FR.
    The impact of recent chemotherapy innovation on the longevity of myeloma patients: US and international evidence.
    Soc Sci Med. 2015 Apr;130:162-71. doi: 10.1016/j.socscimed.2015.02.003. Epub 2015 Feb 7. PubMed PMID: 25703669. [PubMed]
  32. Hsu W, Gonzalez NR, Chien A, Pablo Villablanca J, Pajukanta P, Viñuela F, Bui AA.
    An integrated, ontology-driven approach to constructing observational databases for research.
    J Biomed Inform. 2015 Jun;55:132-42. doi: 10.1016/j.jbi.2015.03.008. Epub 2015 Mar 26. PubMed PMID: 25817919; PubMed Central PMCID: PMC4464942. [PubMed]
  33. Huser V, Sastry C, Breymaier M, Idriss A, Cimino JJ.
    Standardizing Data Exchange for Clinical Research Protocols and Case Report Forms: An Assessment of the Suitability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM).
    J Biomed Inform. 2015 Jul 15. pii: S1532-0464(15)00133-1. doi: 10.1016/j.jbi.2015.06.023. [Epub ahead of print] PubMed PMID: 26188274. [PubMed]
  34. Jacobson RS, Becich MJ, Bollag RJ, Chavan G, Corrigan J, Dhir R, Feldman MD, Gaudioso C, Legowski E, Maihle NJ, Mitchell K, Murphy M, Sakthivel M, Tseytlin E, Weaver J.
    A Federated Network for Translational Cancer Research Using Clinical Data and Biospecimens.
    Cancer Res. 2015 Dec 15;75(24):5194-201. doi:10.1158/0008-5472.CAN-15-1973. Review. PubMed PMID: 26670560; PubMed Central PMCID: PMC4683415.[PubMed]
  35. Jiang G, Evans J, Oniki TA, Coyle JF, Bain L, Huff SM, Kush RD, Chute CG.
    Harmonization of detailed clinical models with clinical study data standards.
    Methods Inf Med. 2015;54(1):65-74. doi: 10.3414/ME13-02-0019. Epub 2014 Nov 26. PubMed PMID: 25426730. [PubMed]
  36. Jiang G, Sohn S, Zimmermann MT, Wang C, Liu H, Chute CG.
    Drug Normalization for Cancer Therapeutic and Druggable Genome Target Discovery.
    AMIA Jt Summits Transl Sci Proc. 2015 Mar 25;2015:72-6. eCollection 2015. PubMed PMID: 26306243; PubMed Central PMCID: PMC4525232. [PubMed]
  37. Keenan CM, Baker JF, Bradley AE, Goodman DG, Harada T, Herbert R, Kaufmann W, Kellner R, Mahler B, Meseck E, Nolte T, Rittinghausen S, Vahle J, Yoshizawa K.
    International Harmonization of Nomenclature and Diagnostic Criteria (INHAND): progress to date and future plans.
    J Toxicol Pathol. 2015 Jan;28(1):51-3. doi:10.1293/tox.2014-0049. Epub 2014 Nov 10. PubMed PMID: 26023262; PubMed Central PMCID: PMC4337500. [PubMed] [PubMed (orig)]
  38. Kim HH, Lee SY, Baik SY, Kim JH.
    MELLO: Medical Lifelog Ontology for Data Terms from Self-Tracking and Lifelog Devices.
    Int J Med Inform. 2015 Dec;84(12):1099-110. doi: 10.1016/j.ijmedinf.2015.08.005. Epub 2015 Aug 17. PubMed PMID: 26383495. [PubMed]
  39. Kim S, Thiessen PA, Bolton EE, Chen J, Fu G, Gindulyte A, Han L, He J, He S, Shoemaker BA, Wang J, Yu B, Zhang J, Bryant SH.
    PubChem Substance and Compound databases.
    Nucleic Acids Res. 2015 Sep 22. pii: gkv951. [Epub ahead of print] PubMed PMID: 26400175.[PubMed]
  40. Kogan A, Alpert K, Ambite JL, Marcus D, Wang L.
    Northwestern University schizophrenia data sharing for SchizConnect: A longitudinal dataset for large-scale integration.
    Neuroimage. 2015 Jun 16. pii: S1053-8119(15)00533-9. doi: 10.1016/j.neuroimage.2015.06.030. [Epub ahead of print] PubMed PMID: 26087378. [PubMed]
  41. Lacson R, Harris K, Brawarsky P, Tosteson TD, Onega T, Tosteson AN, Kaye A, Gonzalez I, Birdwell R, Haas JS.
    Evaluation of an Automated Information Extraction Tool for Imaging Data Elements to Populate a Breast Cancer Screening Registry.
    J Digit Imaging. 2015 Jan 6. [Epub ahead of print] PubMed PMID: 25561069. [PubMed]
  42. Leroux H, Lefort L.
    Semantic enrichment of longitudinal clinical study data using the CDISC standards and the semantic statistics vocabularies.
    J Biomed Semantics. 2015 Apr 9;6:16. doi: 10.1186/s13326-015-0012-6. eCollection 2015. PubMed PMID: 25973166; PubMed Central PMCID: PMC4429421. [PubMed]
  43. Liu L, Tao J, Yang Z, Towfic F.
    Shared Genetic Architecture In Autoimmune Disease - Preliminary Analysis.
    Procs 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2015, Bethesda, MD, US, Nov 9-12, 2015.
  44. Massicano F, Sasso1 A, Amaral-Silva1 H, Oleynik M, Nobrega1 C, Patrão DFC.
    An Ontology for TNM Clinical Stage Inference.
    Proc Brazilian Seminar on Ontologies (ONTOBRAS 2015). São Paulo, Brazil, Sept 8-11, 2015. [PDF Exit Disclaimer logo ]
  45. McIntosh LD, Sharma MK, Mulvihill D, Gupta S, Juehne A, George B, Khot SB, Kaushal A, Watson MA, Nagarajan R.
    caTissue suite to OpenSpecimen: Developing an extensible, open source, web-based biobanking management system.
    J Biomed Inform. 2015 Aug 29. pii: S1532-0464(15)00188-4. doi: 10.1016/j.jbi.2015.08.020. [Epub ahead of print] PubMed PMID: 26325296. [PubMed]
  46. Perrone RD, Neville J, Chapman AB, Gitomer BY, Miskulin DC, Torres VE, Czerwiec FS, Dennis E, Kisler B, Kopko S, Krasa HB, LeRoy E, Castedo J, Schrier RW, Broadbent S.
    Therapeutic Area Data Standards for Autosomal Dominant Polycystic Kidney Disease: A Report From the Polycystic Kidney Disease Outcomes Consortium (PKDOC).
    Am J Kidney Dis. 2015 Jun 15. pii: S0272-6386(15)00758-1. doi: 10.1053/j.ajkd.2015.04.044. [Epub ahead of print] PubMed PMID: 26088508. [PubMed]
  47. Quanico J, Franck J, Gimeno JP, Sabbagh R, Salzet M, Day R, Fournier I.
    Parafilm-assisted microdissection: a sampling method for mass spectrometry-based identification of differentially expressed prostate cancer protein biomarkers.
    Chem Commun (Camb). 2015 Mar 18;51(22):4564-7. doi: 10.1039/c4cc08331h. PubMed PMID: 25490716. [PubMed]
  48. Scholtalbers J, Boegel S, Bukur T, Byl M, Goerges S, Sorn P, Loewer M, Sahin U, Castle JC.
    TCLP: an online cancer cell line catalogue integrating HLA type, predicted neo-epitopes, virus and gene expression.
    Genome Med. 2015 Nov 20;7(1):118. PubMed PMID: 26589293.[PubMed]
  49. Subramanian SL, Kitchen RR, Alexander R, Carter BS, Cheung KH, Laurent LC, Pico A, Roberts LR, Roth ME, Rozowsky JS, Su AI, Gerstein MB, Milosavljevic A.
    Integration of extracellular RNA profiling data using metadata, biomedical ontologies and Linked Data technologies.
    J Extracell Vesicles. 2015 Aug 28;4:27497. doi: 10.3402/jev.v4.27497. eCollection 2015. PubMed PMID: 26320941. [PubMed]
  50. van Soest JPA, Dekker ALAJ, Georgi Nalbantov G.
    Application of Machine Learning for Multicenter Learning.
    Machine Learning in Radiation Oncology. El Naqa I, Li R, Murphy MJ, eds. Springer International Publishing; 2015, pp. 71-97. [Springer Exit Disclaimer logo ]
  51. Wu TJ, Schriml LM, Chen QR, Colbert M, Crichton DJ, Finney R, Hu Y, Kibbe WA, Kincaid H, Meerzaman D, Mitraka E, Pan Y, Smith KM, Srivastava S, Ward S, Yan C, Mazumder R.
    Generating a focused view of disease ontology cancer terms for pan-cancer data integration and analysis.
    Database (Oxford). 2015 Apr 4;2015:bav032. doi: 10.1093/database/bav032. Print 2015. PubMed PMID: 25841438; PubMed Central PMCID: PMC4385274. [PubMed]

    2014
  52. Acosta-Martin AE, Lane L.
    Combining bioinformatics and MS-based proteomics: clinical implications.
    Expert Rev Proteomics. 2014 Jun;11(3):269-84. doi: 10.1586/14789450.2014.900446. Epub 2014 Apr 10. PubMed PMID: 24720436. [PubMed]
  53. Brechat B, Thiessard FMF, Jouhet V.
    Mapping de terminologies diagnostiques en cancérologie par l'intermédiaire du NCI Metathesaurus / Mapping of diagnostic terminologies in oncology using the NCI Metathesaurus.
    Proc / Actes 15es Journées francophones d'informatique médicale (JFIM 2014) co-located with 2e Congrès National d'Informatique Médicale (CNIM 2014). Fes, Morocco, June 12-13, 2014. (in CEUR Workshop Proceedings, Vol-1379, CEUR-WS.org, June 2014). [PDF Exit Disclaimer logo ]
  54. Cai MC, Xu Q, Pan YJ, Pan W, Ji N, Li YB, Jin HJ, Liu K, Ji ZL.
    ADReCS: an ontology database for aiding standardization and hierarchical classification of adverse drug reaction terms.
    Nucleic Acids Res. 2014 Oct 31. pii: gku1066. [Epub ahead of print] PubMed PMID: 25361966. [PubMed]
  55. Carrell DS, Halgrim S, Tran DT, Buist DS, Chubak J, Chapman WW, Savova G.
    Using Natural Language Processing to Improve Efficiency of Manual Chart Abstraction in Research: The Case of Breast Cancer Recurrence.
    Am J Epidemiol. 2014 Mar 15;179(6):749-58. doi: 10.1093/aje/kwt441. Epub 2014 Jan 30. PubMed PMID: 24488511. [PubMed]
  56. Doods J, Botteri F, Dugas M, Fritz F; EHR4CR WP7.
    A European inventory of common electronic health record data elements for clinical trial feasibility.
    Trials. 2014 Jan 10;15(1):18. doi: 10.1186/1745-6215-15-18. PubMed PMID:24410735. [PubMed] [PDF Exit Disclaimer logo ]
  57. Johnson D, Connor AJ, McKeever S, Wang Z, Deisboeck TS, Quaiser T, Shochat E.
    Semantically linking in silico cancer models.
    Cancer Inform. 2014 Dec 8;13(Suppl 1):133-43. doi: 10.4137/CIN.S13895. eCollection 2014. PubMed PMID: 25520553; PubMed Central PMCID: PMC4260769. [PubMed Exit Disclaimer logo ]
  58. Jouhet V, Bréchat B, Mougin F, Thiessard F.
    Intégration de terminologies diagnostiques en cancérologie: le NCI thésaurus comme pivot?
    Rev Epidemiol Sante Publique. 2014 Sept;62(Suppl.5):S185.  [ScienceDirect]
  59. Kahn MG, Bailey LC, Forrest CB, Padula MA, Hirschfeld S.
    Building a Common Pediatric Research Terminology for Accelerating Child Health Research.
    Pediatrics, 2014. Available online Feb 17, 2014. DOI: 10.1542/peds.2013-1504. [Online Exit Disclaimer logo ]
  60. Keenan CM, Goodman DG.
    Regulatory Forum Commentary: Through the Looking Glass--SENDing the Pathology Data We Have INHAND.
    Toxicol Pathol. 2014 July, 42(5):807-810. Epub 2013 Apr 18. PubMed PMID: 23599411. [PubMed]
  61. Kibbe WA, Arze C, Felix V, Mitraka E, Bolton E, Fu G, Mungall CJ, Binder JX, Malone J, Vasant D, Parkinson H, Schriml LM.
    Disease Ontology 2015 update: an expanded and updated database of human diseases for linking biomedical knowledge through disease data.
    Nucleic Acids Res. 2014 Oct 27. pii: gku1011. [Epub ahead of print] PubMed PMID: 25348409. [PubMed Exit Disclaimer logo ]
  62. Köpcke F, Prokosch HU.
    Employing computers for the recruitment into clinical trials: a comprehensive systematic review.
    J Med Internet Res. 2014 Jul 1;16(7):e161. doi: 10.2196/jmir.3446. PubMed PMID: 24985568; PubMed Central PMCID: PMC4128959. [PubMed Exit Disclaimer logo ]
  63. Kurian AW, Mitani A, Desai M, Yu PP, Seto T, Weber SC, Olson C, Kenkare P, Gomez SL, de Bruin MA, Horst K, Belkora J, May SG, Frosch DL, Blayney DW, Luft HS, Das AK.
    Breast cancer treatment across health care systems: linking electronic medical records and state registry data to enable outcomes research.
    Cancer. 2014 Jan 1;120(1):103-11. doi: 10.1002/cncr.28395. Epub 2013 Sep 24. PubMed PMID: 24101577; PubMed Central PMCID: PMC3867595. [PubMed]
  64. Lee HJ, Dang TC, Lee H, Park JC.
    OncoSearch: cancer gene search engine with literature evidence.
    Nucleic Acids Res. 2014 Jul;42(Web Server issue):W416-21. doi: 10.1093/nar/gku368. Epub 2014 May 9. PubMed PMID: 24813447; PubMed Central PMCID: PMC4086113. [PubMed] [Full Text Exit Disclaimer logo ]
  65. Meier A.
    Challenges in Processing Clinical Lab Data.
    PharmaSUG 2014 Conference Proceedings. San Diego, CA, USA, June 1-4, 2014. [PDF Exit Disclaimer logo ]
  66. Meldolesi E, van Soest J, Alitto AR, Autorino R, Dinapoli N, Dekker A, Gambacorta MA, Gatta R, Tagliaferri L, Damiani A, Valentini V.
    VATE: VAlidation of high TEchnology based on large database analysis by learning machine.
    Colorectal Cancer. 3(5)435-450 , DOI 10.2217/crc.14.34 (doi:10.2217/crc.14.34). [Online Exit Disclaimer logo ]
  67. Meldolesi E, van Soest J, Dinapoli N, Dekker A, Damiani A, Gambacorta MA, Valentini V.
    An umbrella protocol for standardized data collection (SDC) in rectal cancer: a prospective uniform naming and procedure convention to support personalized medicine.
    Radiother Oncol. 2014 Jul;112(1):59-62. doi:10.1016/j.radonc.2014.04.008. Epub 2014 May 19. PubMed PMID: 24853366. [PubMed]
  68. Min H, Ohira R, Collins MA, Bondy J, Avis NE, Tchuvatkina O, Courtney PK, Moser RP, Shaikh AR, Hesse BW, Cooper M, Reeves D, Lanese B, Helba C, Miller SM, Ross EA.
    Sharing behavioral data through a grid infrastructure using data standards.
    J Am Med Inform Assoc. 2014 Jul-Aug;21(4):642-9. doi: 10.1136/amiajnl-2013-001763. Epub 2013 Sep 27. PubMed PMID: 24076749; PubMed Central PMCID: PMC4078270. [PubMed Exit Disclaimer logo ]
  69. Mongkolwat P, Kleper V, Talbot S, Rubin D.
    The National Cancer Informatics Program (NCIP) Annotation and Image Markup (AIM) Foundation Model.
    J Digit Imaging. 2014 Dec;27(6):692-701. doi: 10.1007/s10278-014-9710-3. PubMed PMID: 24934452. [PubMed Exit Disclaimer logo ]
  70. Pang C, Hendriksen D, Dijkstra M, van der Velde KJ, Kuiper J, Hillege H, Swertz M.
    BiobankConnect: software to rapidly connect data elements for pooled analysis across biobanks using ontological and lexical indexing.
    J Am Med Inform Assoc. 2014 Oct 31. pii: amiajnl-2013-002577. doi: 10.1136/amiajnl-2013-002577. [Epub ahead of print] PubMed PMID: 25361575. [PubMed]
  71. Sahoo SS, Tao S, Parchman A, Luo Z, Cui L, Mergler P, Lanese R, Barnholtz-Sloan JS, Meropol NJ, Zhang GQ.
    Trial prospector: matching patients with cancer research studies using an automated and scalable approach.
    Cancer Inform. 2014 Dec 4;13:157-66. doi: 10.4137/CIN.S19454. eCollection 2014. PubMed PMID: 25506198; PubMed Central PMCID: PMC4259509. [PubMed Exit Disclaimer logo ]
  72. Santos RS, de Oliveira JM, Malheiros SM, Neto MAP.
    Using reference ontology to elicit attributes of brain tumors database.
    Information Society (i-Society), 2014 International Conference on.London, UK, 10-12 Nov. 2014, pp.181-186, doi: 10.1109/i-Society.2014.7009038 . [IEEE Exit Disclaimer logo ]
  73. Tamir A, Jag U, Sarojini S, Schindewolf C, Tanaka T, Gharbaran R, Patel H, Sood A, Hu W, Patwa R, Blake P, Chirina P, Oh Jeong J, Lim H, Goy A, Pecora A, Suh K.
    Kallikrein family proteases KLK6 and KLK7 are potential early detection and diagnostic biomarkers for serous and papillary serous ovarian cancer subtypes.
    J Ovarian Res. 2014 Dec 5;7(1):109. [Epub ahead of print] PubMed PMID: 25477184; PubMed Central PMCID: PMC4271347. [PubMed Exit Disclaimer logo ]
  74. Tradigo G, Veneziano C, Greco S, Veltri P.
    An Architecture for Integrating Genetic and Clinical Data.
    14th International Conference on Computational Science (ICCS 2014). Procedia Computer Science, Volume 29, 2014, Pages 1959-1969. doi: 10.1016/j.procs.2014.05.180 .
  75. Treepongkaruna S, Simakachorn N, Pienvichit P, Varavithya W, Tongpenyai Y, Garnier P, Mathiex-Fortunet H.
    A randomised, double-blind study of polyethylene glycol 4000 and lactulose in the treatment of constipation in children.
    BMC Pediatr. 2014 Jun 19;14:153. doi: 10.1186/1471-2431-14-153. PubMed PMID: 24943105; PubMed Central PMCID: PMC4075982. [PubMed]
  76. Winnenburg R, Bodenreider O.
    Coverage of Phenotypes in Standard Terminologies.
    Proc Phenotype Day at ISMB 2014. Boston, MA, USA, July 12, 2014. [PDF Exit Disclaimer logo ]
  77. Zunner C, Ganslandt T, Prokosch HU, Bürkle T.
    A reference architecture for semantic interoperability and its practical application.
    Stud Health Technol Inform. 2014;198:40-6. PubMed PMID: 24825683. [PubMed]
     
    2013
  78. Abate F, Acquaviva A, Ficarra E, Piva R, Macii E.
    Gelsius: A Literature-Based Workflow for Determining Quantitative Associations Between Genes and Biological Processes.
    IEEE/ACM Trans Comput Biol Bioinform. 2013 May-Jun;10(3):619-31. doi: 10.1109/TCBB.2013.11. PubMed PMID: 24091396 prev. 23439219. [PubMed] [prev.]
  79. Abate F, Acquaviva A, Ficarra E, Macii E.
    Integration of literature with heterogeneous information for genes correlation scoring.
    ACM Journal on Emerging Technologies in Computing Systems. 2013 Nov 9(4) Article number 28. [ACM Exit Disclaimer logo ]
  80. Anderson HV, Weintraub WS, Radford MJ, Kremers MS, Roe MT, Shaw RE, Pinchotti DM, Tcheng JE.
    Standardized Cardiovascular Data for Clinical Research, Registries, and Patient Care: A Report from the Data Standards Workgroup of the National Cardiovascular Research Infrastructure Project.
    J Am Coll Cardiol. 2013 May 7;61(18):1835-46. doi: 10.1016/j.jacc.2012.12.047. Epub 2013 Mar 6. PubMed PMID: 23500238. [PubMed]
  81. Buckler AJ, Liu TT, Savig E, Suzek BE, Rubin DL, Paik D.
    Quantitative Imaging Biomarker Ontology (QIBO) for Knowledge Representation of Biomedical Imaging Biomarkers.
    J Digit Imaging. 2013 Aug;26(4):630-41. PubMed PMID: 23589184; PubMed Central PMCID: PMC3705004. [PubMed]
  82. Buckler AJ, Paik D, Ouellette M, Danagoulian J, Wernsing G, Suzek BE.
    A Novel Knowledge Representation Framework for the Statistical Validation of Quantitative Imaging Biomarkers.
    J Digit Imaging. Aug;26(4):614-29. doi: 10.1007/s10278-013-9598-3. PubMed PMID: 23546775; PubMed Central PMCID: PMC3705009. [PubMed]
  83. Chang JF, Popescu M, Arthur GL.
    Automated extraction of precise protein expression patterns in lymphoma by text mining abstracts of immunohistochemical studies.
    J Pathol Inform. 2013 Jul 31;4:20. doi: 10.4103/2153-3539.115880. eCollection 2013. PubMed PMID: 23967385; PubMed Central PMCID: PMC3746413. [PubMed]
  84. Cimino JJ, Ayres EJ, Remennik L, Rath S, Freedman R, Beri A, Chen Y, Huser V.
    The National Institutes of Health's Biomedical Translational Research Information System (BTRIS): Design, contents, functionality and experience to date.
    J Biomed Inform. 2013 Nov 19. pii: S1532-0464(13)00181-0. doi: 10.1016/j.jbi.2013.11.004. [Epub ahead of print] PubMed PMID: 24262893. [PubMed]
  85. da Silva KR, Costa R, Crevelari ES, Lacerda MS, de Moraes Albertini CM, Filho MM, Santana JE, Vissoci JR, Pietrobon R, Barros JV.
    Glocal clinical registries: pacemaker registry design and implementation for global and local integration - methodology and case study.
    PLoS One. 2013 Jul 25;8(7):e71090. doi:10.1371/journal.pone.0071090. Print 2013. PubMed PMID: 23936257. [PubMed]
  86. Gómez-Pérez A, Martínez-Romero M, Rodríguez-González A, Vázquez G, Vázquez-Naya JM.
    Ontologies in medicinal chemistry: current status and future challenges.
    Curr Top Med Chem. 2013 Mar 1;13(5):576-90. PubMed PMID: 23548021. [PubMed]
  87. Goss FR, Zhou L, Plasek JM, Broverman C, Robinson G, Middleton B, Rocha RA.
    Evaluating standard terminologies for encoding allergy information.
    J Am Med Inform Assoc. 2013 Sep-Oct;20(5):969-79. doi: 10.1136/amiajnl-2012-000816. Epub 2013 Feb 9. PubMed PMID: 23396542. [PubMed]
  88. Grove MJ.
    Development of an Ontology for Rehabilitation: Traumatic Brain Injury.
    Thesis (Ph.D.) University of Minnesota, 2013. [U.MN Exit Disclaimer logo ]
  89. Pan H, Ardini MA, Bakalov V, Delatte M, Eggers P, Ganapathi L, Hollingsworth CR, Levy J, Li S, Pratt J, Pugh N, Qin Y, Rasooly R, Ray H, Richardson JE, Flynn Riley A, Rogers SM, Tan S, Turner CF, White S, Cooley PC.
    'What's in the NIDDK CDR?'--public query tools for the NIDDK central data repository.
    Database (Oxford). 2013 Feb 8;2013:bas058. doi: 10.1093/database/bas058. Print 2013. PubMed PMID: 23396299. [PubMed]
  90. Petkov VI, Penberthy LT, Dahman BA, Poklepovic A, Gillam CW, McDermott JH.
    Automated determination of metastases in unstructured radiology reports for eligibility screening in oncology clinical trials.
    Exp Biol Med (Maywood). 2013 Dec;238(12):1370-8. doi: 10.1177/1535370213508172. Epub 2013 Oct 9. PubMed PMID: 24108448. [PubMed]
  91. Phan JH, Young AN, Wang MD.
    omniBiomarker: a Web-Based Application for Knowledge-Driven Biomarker Identification.
    IEEE Trans Biomed Eng. Dec 2013, 60(12):3364-3367. PubMed PMID: 22893372. [PubMed] [IEEE Exit Disclaimer logo ]
  92. Ries M, Prokosch HU, Beckmann MW, Bürkle T.
    Single-Source Tumor Documentation – Reusing Oncology Data for Different Purposes.
    Onkologie 2013;36(3):136-41. doi: 10.1159/000348528. Epub 2013 Feb 21. PubMed PMID: 23486003. [PubMed]
  93. Santos RS, Malheiros SM, Cavalheiro S, de Oliveira JM.
    A data mining system for providing analytical information on brain tumors to public health decision makers.
    Comput Methods Programs Biomed. 2013 Mar;109(3):269-82. doi: 10.1016/j.cmpb.2012.10.010. Epub 2012 Oct 31. PubMed PMID: 23122302. [PubMed]
  94. Zhu Q, Freimuth RR, Lian Z, Bauer S, Pathak J, Tao C, Durski MJ, Chute CG.
    Harmonization and semantic annotation of data dictionaries from the Pharmacogenomics Research Network: A case study.
    J Biomed Inform. 2013 Apr;46(2):286-93. doi: 10.1016/j.jbi.2012.11.004. Epub 2012 Nov 29. PubMed PMID: 23201637; PubMed Central PMCID: PMC3606279. [PubMed]
     
    2012
  95. Chen AP, Setser A, Anadkat MJ, Cotliar J, Olsen EA, Garden BC, Lacouture ME.
    Grading dermatologic adverse events of cancer treatments: the Common Terminology Criteria for Adverse Events Version 4.0.
    J Am Acad Dermatol. 2012 Nov;67(5):1025-39. doi: 10.1016/j.jaad.2012.02.010. Epub 2012 Apr 11. PubMed PMID: 22502948. [PubMed]
  96. Cuggia M, Dufour J-C, Zekri O, Gibaud I, Garde C, Bohec C, Duvauferrier R, Fieschi D, Besana P, Charlois L, Bourde A, Garcelon N, Laurent J, Fieschi M, Dameron O.
    Système sémantiquement interopérable de sélection semi-automatique des patients éligibles aux essais thérapeutiques en cancérologie [ASTEC: Automatic selection of clinical trials based on eligibility criteria].
    IRBM, 2012. In Press. Available online 6 March 2012. [http://dx.doi.org/10.1016/j.irbm.2012.02.001 Exit Disclaimer logo ]
  97. de Bree E, Romanos J, Tsogkas J, Askoxylakis J, Metaxari M, Michalakis J, Volakakis E, Melissas J, Tsiftsis DD.
    Complications and Toxicity After Abdominal and Pelvic Hypoxic Stop-Flow Perfusion Chemotherapy: Incidence and Assessment of Risk Factors.
    Ann Surg Oncol. 2012 Oct;19(11):3591-7. doi:10.1245/s10434-012-2383-6. Epub 2012 May 11. PubMed PMID: 22576062. [PubMed]
  98. Donfack Guefack V, Bertaud Gounot V, Duvauferrier R, Bourde A, Morelli J, Lasbleiz J.
    Ontology driven decision support systems for medical diagnosis - an interactive form for consultation in patients with plasma cell disease.
    Stud Health Technol Inform. 2012;180:108-12. PubMed PMID: 22874162. [PubMed]
  99. Elkin PL.
    Springer Terminology Related Standards Development.
    In Elkin PL, ed. Terminology and Terminological Systems, Ch.7 pp.107-123, Springer London, 2012. [Springer Exit Disclaimer logo ]
  100. Gazder Y.
    Scoping the Processes and Feasibility of a Synoptic Breast Pathology Reporting Module.
    Dalhousie University Department of Diagnostic Radiology Internship Report 2012 Aug 10, Halifax, Nova Scotia, Canada. [PDF Exit Disclaimer logo ]
  101. Groza T, Hunter J, Zankl A.
    The Bone Dysplasia Ontology: Integrating genotype and phenotype information in the skeletal dysplasia domain.
    BMC Bioinformatics. 2012 Mar 26;13:50. doi: 10.1186/1471-2105-13-50. PubMed PMID: 22449239; PubMed Central PMCID: PMC3338382. [PubMed]
  102. Hayamizu TF, de Coronado S, Fragoso G, Sioutos N, Kadin JA, Ringwald M.
    The mouse-human anatomy ontology mapping project.
    Database (Oxford). 2012 Mar 20;2012:bar066. Print 2012. PubMed PMID: 22434834. [PubMed] [Full Text Exit Disclaimer logo ]
  103. Ho Sui SJ, Begley K, Reilly D, Chapman B, McGovern R, Rocca-Sera P, Maguire E, Altschuler GM, Hansen TA, Sompallae R, Krivtsov A, Shivdasani RA, Armstrong SA, Culhane AC, Correll M, Sansone SA, Hofmann O, Hide W.
    The Stem Cell Discovery Engine: an integrated repository and analysis system for cancer stem cell comparisons.
    Nucleic Acids Res. 2012 Jan;40(Database issue):D984-91. Epub 2011 Nov 24. PubMed PMID: 22121217; PubMed Central PMCID: PMC3245064. [PubMed]
  104. Hsu W, Taira RK, El-Saden S, Kangarloo H, Bui AA.
    Context-based electronic health record: toward patient specific healthcare.
    IEEE Trans Inf Technol Biomed. 2012 Mar;16(2):228-34. doi: 10.1109/TITB.2012.2186149. PubMed PMID: 22395637; PubMed Central PMCID: PMC4414061. [PubMed]
  105. Machado CM, Couto FM, Fernandes AR, Santos S, Freitas AT.
    Toward a Translational Medicine Approach for Hypertrophic Cardiomyopathy.
    Proc 3rd International Conference on Information Technology in Bio- and Medical Informatics (ITBAM 2012), Vienna, Austria, September 4-5, 2012 (LNCS 7451:151-165). [PDF Exit Disclaimer logo ] [DOI Exit Disclaimer logo ]
  106. McCullough CE, Reed TL, Kaufman-Rivi D.
    A Tool to Analyze Medical Device Problems: The Food and Drug Administration Device Problem Codes.
    J Clinical Engineering. 2012 Apr/Jun;37(2):56–62. doi: 10.1097/JCE.0b013e31824c99f1 [Online Exit Disclaimer logo ]
  107. Milacic M, Haw R, Rothfels K, Wu G, Croft D, Hermjakob H, D’Eustachio P, Stein L.
    Annotating Cancer Variants and Anti-Cancer Therapeutics in Reactome.
    Cancers. 2012 4(4), 1180-1211; doi:10.3390/cancers4041180. [PDF Exit Disclaimer logo ]
  108. Parikh PP, Minning TA, Nguyen V, Lalithsena S, Asiaee AH, Sahoo SS, Doshi P, Tarleton R, Sheth AP.
    A Semantic Problem Solving Environment for Integrative Parasite Research: Identification of Intervention Targets for Trypanosoma cruzi.
    PLoS Negl Trop Dis. 2012 Jan;6(1):e1458. Epub 2012 Jan 17. PubMed PMID: 22272365; PubMed Central PMCID: PMC3260319. [PubMed]
  109. Parker JL, Lushina N, Bal PS, Petrella T, Dent R, Lopes G.
    Impact of biomarkers on clinical trial risk in breast cancer.
    Breast Cancer Res Treat. 2012 Nov;136(1):179-85. doi: 10.1007/s10549-012-2247-6. Epub 2012 Sep 25. PubMed PMID: 23007573. [PubMed] [PDF Exit Disclaimer logo ]
  110. Schriml LM, Arze C, Nadendla S, Chang YW, Mazaitis M, Felix V, Feng G, Kibbe WA.
    Disease Ontology: a backbone for disease semantic integration.
    Nucleic Acids Res. 2012 Jan;40(Database issue):D940-6. Epub 2011 Nov 12. PubMed PMID: 22080554; PubMed Central PMCID: PMC3245088. [PubMed]
  111. Wang H, Yatawara M, Huang S, Dudley K, Szekely C, Holden S, Piantadosi S.
    The Integrated Proactive Surveillance System for Prostate Cancer.
    Open Med Inform J. 2012;6:1-8. Epub 2012 Mar 2. PubMed PMID: 22505956. [PubMed] [PDF Exit Disclaimer logo ]
     
    2011
  112. Adams N, Hoehndorf R, Gkoutos GV, Hansen G, Hennig C.
    PIDO: The Primary Immunodeficiency Disease Ontology.
    Bioinformatics. 2011 Nov 15;27(22):3193-9. doi: 10.1093/bioinformatics/btr531. Epub 2011 Sep 22. PubMed PMID: 21949270. [PubMed]
  113. Bertaud-Gounot V, Donfack V, Lasbleiz J, Bourde A, Duvauferrier R.
    Creating an ontology driven rules base for an expert system for medical diagnosis.
    Stud Health Technol Inform. 2011;169:714-8. PubMed PMID: 21893840. [PubMed]
  114. Chen S, Hsu C.
    The TCR Cancer Registry Repository for Annotating Cancer Data.
    Emergency Management and Management Sciences (ICEMMS), 2011 2nd IEEE International Conference on, 2011 Aug 8-10:297-300. [IEEE Exit Disclaimer logo ]
  115. Chute CG, Pathak J, Savova GK, Bailey KR, Schor MI, Hart LA, Beebe CE, Huff SM.
    The SHARPn project on secondary use of Electronic Medical Record data: progress, plans, and possibilities.
    AMIA Annu Symp Proc. 2011;2011:248-56. Epub 2011 Oct 22. PubMed PMID: 22195076; PubMed Central PMCID: PMC3243296. [PubMed]
  116. Cuggia M, Bourde A, Turlin B, Vincendeau S, Bertaud V, Bohec C, Duvauferrier R.
    Automatic Definition of the Oncologic EHR Data Elements from NCIT in OWL.
    Stud Health Technol Inform. 2011;169:517-21. PubMed PMID: 21893803. [PubMed]
  117. Foran DJ, Yang L, Chen W, Hu J, Goodell LA, Reiss M, Wang F, Kurc T, Pan T, Sharma A, Saltz JH.
    ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology.
    J Am Med Inform Assoc. 2011 Jul-Aug;18(4):403-15. doi: 10.1136/amiajnl-2011-000170. Epub 2011 May 23. PubMed PMID: 21606133; PubMed Central PMCID: PMC3128405. [PubMed]
  118. Grenon P, Wimalaratne S, de Bono B.
    Ontology-Strength Industry Standards: The Case of the Clinical Trial Domain.
    Frontiers in Artificial Intelligence and Applications, Volume 229: Formal Ontologies Meet Industry, Proc Fifth International Workshop, FOMI 2011, July 7-8, 2011, Delft, Netherlands. (eds. Vermaas PE, Dignum V) IOS Press 2011 pp.41-49. ISBN 978-1-60750-784-0 (print) 978-1-60750-785-7 (online), DOI 10.3233/978-1-60750-785-7-41 [IOS Press Exit Disclaimer logo ]
  119. Hu H, Correll M, Kvecher L, Osmond M, Clark J, Bekhash A, Schwab G, Gao D, Gao J, Kubatin V, Shriver CD, Hooke JA, Maxwell LG, Kovatich AJ, Sheldon JG, Liebman MN, Mural RJ.
    DW4TR: A Data Warehouse for Translational Research.
    J Biomed Inform. 2011 Dec;44(6):1004-19. PubMed PMID: 21872681. [PubMed]
  120. Kush RD.
    Interoperability for the Learning Health System.
    Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care: Workshop Series Summary. Grossmann C, Powers B, McGinnis JM eds. Institute of Medicine (2011) pp.108-114. [Online Exit Disclaimer logo ]
  121. Lasbleiz J, Brillet E, Decaux O, Duvauferrier R, Bertaud-Gounot V.
    Staging disease with Protégé 4: Example of multiple myeloma.
    IRBM, 2011 32(6):329-331. Available online 21 November 2011, doi:10.1016/j.irbm.2011.10.003
  122. Li X, Wang Q, Zheng Y, Lv S, Ning S, Sun J, Huang T, Zheng Q, Ren H, Xu J, Wang X, Li Y.
    Prioritizing human cancer microRNAs based on genes' functional consistency between microRNA and cancer.
    Nucleic Acids Res. 2011 Dec;39(22):e153. Epub 2011 Oct 5. PubMed PMID: 21976726; PubMed Central PMCID: PMC3239203. [PubMed Exit Disclaimer logo ]
  123. Liu H, Burkhart Q, Bell DS.
    Evaluation of the NCPDP Structured and Codified Sig Format for e-prescriptions.
    J Am Med Inform Assoc. 2011 Sep 1;18(5):645-51. Epub 2011 May 25. PubMed PMID: 21613642; PubMed Central PMCID: PMC3168301. [PubMed]
  124. Moser RP, Hesse BW, Shaikh AR, Courtney P, Morgan G, Augustson E, Kobrin S, Levin KY, Helba C, Garner D, Dunn M, Coa K.
    Grid-enabled measures: using Science 2.0 to standardize measures and share data.
    Am J Prev Med. 2011 May;40(5 Suppl 2):S134-43. PubMed PMID: 21521586; PubMed Central PMCID: PMC3088871. [PubMed]
  125. Overton JA, Romagnoli C, Chhem R.
    Open biomedical ontologies applied to prostate cancer.
    Applied Ontology 2011;6(1):35-51. [ACM DL Exit Disclaimer logo ]
  126. Pathak J, Wang J, Kashyap S, Basford M, Li R, Masys DR, Chute CG.
    Mapping clinical phenotype data elements to standardized metadata repositories and controlled terminologies: the eMERGE Network experience.
    J Am Med Inform Assoc. 2011 Jul-Aug;18(4):376-86. Epub 2011 May 19. PubMed PMID: 21597104; PubMed Central PMCID: PMC3128396. [PubMed]
  127. Schad PA, Mobley LR, Hamilton CM.
    Building a biomedical cyberinfrastructure for collaborative research.
    Am J Prev Med. 2011 May;40(5 Suppl 2):S144-50. PubMed PMID: 21521587. [PubMed]
  128. Sherman S, Shats O, Fleissner E, Bascom G, Yiee K, Copur M, Crow K, Rooney J, Mateen Z, Ketcham MA, Feng J, Sherman A, Gleason M, Kinarsky L, Silva-Lopez E, Edney J, Reed E, Berger A, Cowan K.
    Multicenter breast cancer collaborative registry.
    Cancer Inform. 2011;10:217-26. Epub 2011 Aug 31. PubMed PMID: 21918596; PubMed Central PMCID: PMC3169352. [PubMed]
  129. Sherman S, Shats O, Ketcham MA, Anderson MA, Whitcomb DC, Lynch HT, Ghiorzo P, Rubinstein WS, Sasson AR, Grizzle WE, Haynatzki G, Feng J, Sherman A, Kinarsky L, Brand RE.
    PCCR: Pancreatic Cancer Collaborative Registry.
    Cancer Inform. 2011 Mar 23;10:83-91. PubMed PMID: 21552494; PubMed Central PMCID: PMC3085425. [PubMed]
  130. Warden GI, Lacson R, Khorasani R.
    Leveraging Terminologies for Retrieval of Radiology Reports with Critical Imaging Findings.
    AMIA Annu Symp Proc. 2011 Oct;2011:1481-8. Epub 2011 Oct 22. PubMed PMID: 22195212; PubMed Central PMCID: PMC3243125. [PubMed]
  131. Yong MY, Gonzalez-Beltran A, Begent R.
    Establishing a knowledge trail from molecular experiments to clinical trials.
    N Biotechnol. 2011 Sep;28(5):464-80. Epub 2011 Apr 5. PubMed PMID: 21473938. [PubMed]
     
    2010
  132. Besana P, Cuggia M, Zekri O, Bourde A, Burgun A.
    Using Semantic Web Technologies for Clinical Trial Recruitment.
    Proc. 9th Intl. Semantic Web Conference (ISWC 2010), Vol II, pp. 34-49, Shanghai, China, Nov. 7-11, 2010. [ACM Exit Disclaimer logo ]
  133. Crowley RS, Castine M, Mitchell K, Chavan G, McSherry T, Feldman M.
    caTIES: a grid based system for coding and retrieval of surgical pathology reports and tissue specimens in support of translational research.
    J Am Med Inform Assoc. 2010 May-Jun;17(3):253-64. PubMed PMID: 20442142; PubMed Central PMCID: PMC2995710. [PubMed] [Full Text Exit Disclaimer logo ]
  134. Deus HF, Veiga DF, Freire PR, Weinstein JN, Mills GB, Almeida JS.
    Exposing the cancer genome atlas as a SPARQL endpoint.
    J Biomed Inform. 2010 Dec;43(6):998-1008. PubMed PMID: 20851208; PubMed Central PMCID: PMC3071752. [PubMed]
  135. Haroske G, Kramm T, Mörz M, Oberholzer M.
    Onkologische Datenelemente in der Histopathologie [Oncological data elements in histopathology].
    Pathologe. 2010 Sep;31(5):385-92. doi:10.1007/s00292-010-1289-y. Review. German. PubMed PMID: 20544201. [PubMed]
  136. Hirschfeld S, Songco D, Kramer BS, Guttmacher AE.
    National Children's Study: update in 2010.
    Mt Sinai J Med. 2011 Jan-Feb;78(1):119-25. doi: 10.1002/msj.20227. PubMed PMID: 21259268. [PubMed]
  137. Jiang G, Solbrig HR, Iberson-Hurst D, Kush RD, Chute CG.
    A Collaborative Framework for Representation and Harmonization of Clinical Study Data Elements Using Semantic MediaWiki.
    AMIA Summits Transl Sci Proc. 2010 Mar 1;2010:11-5. PubMed PMID: 21347136; PubMed Central PMCID: PMC3041544. [PubMed]
  138. Kang HP, Borromeo CD, Berman JJ, Becich MJ.
    The tissue microarray OWL schema: An open-source tool for sharing tissue microarray data.
    J Pathol Inform. 2010 Jul 13;1. pii: 9. PubMed PMID: 20805954; PubMed Central PMCID: PMC2929536. [PubMed]
  139. Lu R.
    Biomedical data retrieval utilizing textual data in a gene expression database by Richard Lu, MD.
    Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2010. [URI Exit Disclaimer logo ]
  140. Machado CM, Couto F, Fernandes AR, Santos S, Cardim N, Freitas AT.
    Semantic characterization of hypertrophic cardiomyopathy disease
    2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops, BIBMW 2010, art. no. 5703841, 2010, pp. 432-439. [Scopus Exit Disclaimer logo ]
  141. Martínez-Romero M, Vázquez-Naya JM, Rabuñal JR, Pita-Fernández S, Macenlle R, Castro-Alvariño J, López-Roses L, Ulla JL, Martínez-Calvo AV, Vázquez S, Pereira J, Porto-Pazos AB, Dorado J, Pazos A, Munteanu CR.
    Artificial intelligence techniques for colorectal cancer drug metabolism: ontology and complex network.
    Curr Drug Metab. 2010 May;11(4):347-68. Review. PubMed PMID: 20446907. [PubMed]
  142. Reed TL, Kaufman-Rivi D.
    FDA adverse Event Problem Codes: standardizing the classification of device and patient problems associated with medical device use.
    Biomed Instrum Technol. 2010 May-Jun;44(3):248-56. PubMed PMID: 20715359. [PubMed]
  143. Sim I, Carini S, Tu S, Wynden R, Pollock BH, Mollah SA, Gabriel D, Hagler HK, Scheuermann RH, Lehmann HP, Wittkowski KM, Nahm M, Bakken S.
    The Human Studies Database Project: Federating Human Studies Design Data Using the Ontology of Clinical Research.
    AMIA Summits Transl Sci Proc. 2010: 51–55. [Online]
  144. Vázquez-Naya JM, Martínez-Romero M, Porto-Pazos AB, Novoa F, Valladares-Ayerbes M, Pereira J, Munteanu CR, Dorado J.
    Ontologies of drug discovery and design for neurology, cardiology and oncology.
    Curr Pharm Des. 2010;16(24):2724-36. Review. PubMed PMID: 20642429. [PubMed]
  145. Yip V, Mete M, Topaloglu U, Kockara S.
    Concept Discovery for Pathology Reports using an N-gram Model.
    AMIA Summits Transl Sci Proc. 2010 Mar 1;2010:43-7. PubMed PMID: 21347147; PubMed Central PMCID: PMC3041542. [PubMed]
  146. Yong M, Begent R.
    Best use of experimental data in cancer informatics.
    Future Oncol. 2010 Oct;6(10):1551-62. Review. Erratum in: Future Oncol. 2011 Jan;7(1):154. PubMed PMID: 21062155. [PubMed] [PDF Exit Disclaimer logo ]
     
    2009
  147. Parkinson H, Kapushesky M, Kolesnikov N, Rustici G, Shojatalab M, Abeygunawardena N, Berube H, Dylag M, Emam I, Farne A, Holloway E, Lukk M, Malone J, Mani R, Pilicheva E, Rayner TF, Rezwan F, Sharma A, Williams E, Bradley XZ, Adamusiak T, Brandizi M, Burdett T, Coulson R, Krestyaninova M, Kurnosov P, Maguire E, Neogi SG, Rocca-Serra P, Sansone SA, Sklyar N, Zhao M, Sarkans U, Brazma A.
    ArrayExpress update--from an archive of functional genomics experiments to the atlas of gene expression.
    Nucleic Acids Res. 2009 Jan;37(Database issue):D868-72. Epub 2008 Nov 10. PubMed PMID: 19015125; PubMed Central PMCID: PMC2686529. [PubMed]
  148. Shah NH, Jonquet C, Chiang AP, Butte AJ, Chen R, Musen MA.
    Ontology-driven indexing of public datasets for translational bioinformatics.
    BMC Bioinformatics. 2009 Feb 5;10 Suppl 2:S1. PubMed PMID: 19208184; PubMed Central PMCID: PMC2646250. [PubMed]
  149. Yong M, Tolner B, Nagl S, Pedley RB, Chester K, Green AJ, Mayer A, Sharma S, Begent R.
    Data standards for minimum information collection for antibody therapy experiments.
    Protein Eng Des Sel. 2009 Mar;22(3):221-4. PubMed PMID: 19224941. [PubMed] [Oxford full text Exit Disclaimer logo ]
     
    2008
  150. Kaefer CM, Milner JA.
    The role of herbs and spices in cancer prevention.
    J Nutr Biochem. 2008 Jun;19(6):347-61. Review. PubMed PMID: 18499033; PubMed Central PMCID: PMC2771684. [PubMed] [PubMed Central]
  151. Liu H, Li X, Yoon V, Clarke R.
    Annotating breast cancer microarray samples using ontologies.
    AMIA Annu Symp Proc. 2008 Nov 6:414-8. PubMed PMID: 18999108; PubMed Central PMCID: PMC2655965. [PubMed]
  152. Marinelli RJ, Montgomery K, Liu CL, Shah NH, Prapong W, Nitzberg M, Zachariah ZK, Sherlock GJ, Natkunam Y, West RB, van de Rijn M, Brown PO, Ball CA.
    The Stanford Tissue Microarray Database.
    Nucleic Acids Res. 2008 Jan;36(Database issue):D871-7. Epub 2007 Nov 7. PubMed PMID: 17989087; PubMed Central PMCID: PMC2238948. [PubMed]
     
    2007
  153. Arvanitis TN, Taweel A, Zhao L, et al.
    Supporting E-Trials Over Distributed Networks: A tool for capturing randomised control trials (RCT) eligibility criteria using the National Cancer Institute's (NCI) Enterprise Vocabulary Services (EVS)
    Technol Health Care 2007;15:298-299.
  154. Burgun A, Bodenreider O.
    Issues in integrating epidemiology and research information in oncology: experience with ICD-O3 and the NCI Thesaurus.
    AMIA Annu Symp Proc. 2007 Oct 11:85-9. PubMed PMID: 18693803. [PubMed]
  155. Frey LJ, Maojo V, Mitchell JA.
    Bioinformatics linkage of heterogeneous clinical and genomic information in support of personalized medicine.
    Yearb Med Inform. 2007:98-105. Erratum in: Yearb Med Inform. 2008:19. PubMed PMID: 17700912. [PubMed]
  156. Marquet G, Dameron O, Saikali S, Mosser J, Burgun A.
    Grading glioma tumors using OWL-DL and NCI Thesaurus.
    AMIA Annu Symp Proc. 2007 Oct 11:508-12. PubMed PMID: 18693888; PubMed Central PMCID: PMC2655830. [PubMed]
  157. Rhodes DR, Kalyana-Sundaram S, Mahavisno V, Varambally R, Yu J, Briggs BB, Barrette TR, Anstet MJ, Kincead-Beal C, Kulkarni P, Varambally S, Ghosh D, Chinnaiyan AM.
    Oncomine 3.0: genes, pathways, and networks in a collection of 18,000 cancer gene expression profiles.
    Neoplasia. 2007 Feb;9(2):166-80. PubMed PMID: 17356713; PubMed Central PMCID: PMC1813932. [PubMed]
  158. Richesson RL, Krischer J.
    Data standards in clinical research: gaps, overlaps, challenges and future directions.
    J Am Med Inform Assoc. 2007 Nov-Dec;14(6):687-96. Epub 2007 Aug 21. Erratum in: J Am Med Inform Assoc. 2008. [PubMed]
    Mar-Apr;15(2):265. PubMed PMID: 17712081; PubMed Central PMCID: PMC2213488.
  159. Shah NH, Rubin DL, Espinosa I, Montgomery K, Musen MA.
    Annotation and query of tissue microarray data using the NCI Thesaurus.
    BMC Bioinformatics. 2007 Aug 8;8:296. PubMed PMID: 17686183; PubMed Central PMCID: PMC1988837. [PubMed]
  160. Shifman MA, Li Y, Colangelo CM, Stone KL, Wu TL, Cheung KH, Miller PL, Williams KR.
    YPED: a web-accessible database system for protein expression analysis.
    J Proteome Res. 2007 Oct;6(10):4019-24. Epub 2007 Sep 15. PubMed PMID: 17867667. [PubMed]
     
    2006
  161. Deitzer JR, Payne PR, Starren JB.
    Coverage of clinical trials tasks in existing ontologies.
    AMIA Annu Symp Proc. 2006:903. PubMed PMID: 17238522; PubMed Central PMCID: PMC1839431. [PubMed]
  162. Shah NH, Rubin DL, Supekar KS, Musen MA.
    Ontology-based annotation and query of tissue microarray data.
    AMIA Annu Symp Proc. 2006:709-13. PubMed PMID: 17238433; PubMed Central PMCID: PMC1839511. [PubMed]
  163. Tobias J, Chilukuri R, Komatsoulis GA, Mohanty S, Sioutos N, Warzel DB, Wright LW, Crowley RS.
    The CAP Cancer Protocols – A Case Study of caCORE Based Data Standards Implementation to Integrate with the Cancer Biomedical Informatics Grid.
    BMC Medical Informatics Decision Making, 20; 6:25, 2006. [PubMed] [Free PMC Article]
     
    2005
  164. Bodenreider O, Hayamizu TF, Ringwald M, De Coronado S, Zhang S.
    Of mice and men: aligning mouse and human anatomies.
    AMIA Annu Symp Proc. 2005:61-5. PubMed PMID: 16779002; PubMed Central PMCID: PMC1560846. [PubMed]
  165. Zhang S, Bodenreider O.
    Alignment of multiple ontologies of anatomy: deriving indirect mappings from direct mappings to a reference.
    AMIA Annu Symp Proc. 2005:864-8. PubMed PMID: 16779163; PubMed Central PMCID: PMC1560629. [PubMed]
     
    2003
  166. Chute CG, Carter JS, Tuttle MS, Haber MW, Brown SH.
    Integrating pharmacokinetics knowledge into a drug ontology as an extension to support pharmacogenomics.
    AMIA Annu Symp Proc. 2003:170-4. PubMed PMID: 14728156; PubMed Central PMCID: PMC1480302. [PubMed] [Free PMC Article]
     
    2002
  167. Kogan SC, Ward JM, Anver MR, Berman JJ, Brayton C, Cardiff RD, Carter JS, de Coronado S, Downing JR, Fredrickson TN, Haines DC, Harris AW, Harris NL, Hiai H, Jaffe ES, MacLennan IC, Pandolfi PP, Pattengale PK, Perkins AS, Simpson RM, Tuttle MS, Wong JF, Morse HC 3rd; Hematopathology subcommittee of the Mouse Models of Human Cancers Consortium.
    Bethesda proposals for classification of nonlymphoid hematopoietic neoplasms in mice.
    Blood. 2002 Jul 1;100(1):238-45. PubMed PMID: 12070033. [PubMed]

caCORE, caGrid and caBIG

  1. Boyd LB, Hunicke-Smith SP, Stafford GA, Freund ET, Ehlman M, Chandran U, Dennis R, Fernandez AT, Goldstein S, Steffen D, Tycko B, Klemm JD.
    The caBIG® Life Science Business Architecture Model.
    Bioinformatics. 2011 May 15;27(10):1429-35. Epub 2011 Mar 29. PubMed PMID: 21450709; PubMed Central PMCID: PMC3087952. [PubMed] [Free PMC]
     
    2009
  2. Cimino JJ, Hayamizu TF, Bodenreider O, Davis B, Stafford GA, Ringwald M.
    The caBIG terminology review process.
    J Biomed Inform. 2009 Jun;42(3):571-80. Epub 2008 Dec 25. PubMed PMID: 19154797; PubMed Central PMCID: PMC2729758. [PubMed]
  3. Hastings S, Oster S, Langella S, Melean C, Borlawsky T, Dhavel R, Payne P.
    Adoption and Adaptation of caGrid for CTSA.
    Summit on Translat Bioinforma. 2009 Mar 1;2009:44-8. PubMed PMID: 21347169; PubMed Central PMCID: PMC3041578. [PubMed] [Full Text]
     
    2008
  4. Komatsoulis GA, Warzel DB, Hartel FW, Shanbhag K, Chilukuri R, Fragoso G, Coronado S, Reeves DM, Hadfield JB, Ludet C, Covitz PA.
    caCORE version 3: Implementation of a model driven, service-oriented architecture for semantic interoperability.
    J Biomed Inform. 2008 Feb;41(1):106-23. Epub 2007 Apr 2. PubMed PMID: 17512259; PubMed Central PMCID: PMC2254758. [PubMed]
  5. Oster S, Langella S, Hastings S, Ervin D, Madduri R, Phillips J, Kurc T, Siebenlist F, Covitz P, Shanbhag K, Foster I, Saltz J.
    caGrid 1.0: an enterprise Grid infrastructure for biomedical research.
    J Am Med Inform Assoc. 2008 Mar-Apr;15(2):138-49. Epub 2007 Dec 20. PubMed PMID: 18096909; PubMed Central PMCID: PMC2274794. [PubMed]
  6. Shironoshita EP, Jean-Mary YR, Bradley RM, Kabuka MR.
    semCDI: a query formulation for semantic data integration in caBIG.
    J Am Med Inform Assoc. 2008 Jul-Aug;15(4):559-68. Epub 2008 Apr 24. PubMed PMID: 18436897; PubMed Central PMCID: PMC2442262. [PubMed]
     
    2006
  7. Gao Q, Zhang YL, Xie ZY, Zhang QP, Hu ZZ.
    caCORE: core architecture of bioinformation on cancer research in America.
    Beijing Da Xue Xue Bao. 2006 Apr 18;38(2):218-21. Chinese. PubMed PMID: 16617371. [PubMed]
  8. Phillips J, Chilukuri R, Fragoso G, Warzel D, Covitz PA.
    The caCORE Software Development Kit: streamlining construction of interoperable biomedical information services.
    BMC Med Inform Decis Mak. 2006 Jan 6;6:2. PubMed PMID: 16398930; PubMed Central PMCID: PMC1379637. [PubMed]
  9. Saltz J, Oster S, Hastings S, Langella S, Kurc T, Sanchez W, Kher M, Manisundaram A, Shanbhag K, Covitz P.
    caGrid: design and implementation of the core architecture of the cancer biomedical informatics grid.
    Bioinformatics. 2006 Aug 1;22(15):1910-6. Epub 2006 Jun 9. PubMed PMID: 16766552. [PubMed]
     
    2003
  10. Covitz PA, Hartel F, Schaefer C, De Coronado S, Fragoso G, Sahni H, Gustafson S, Buetow KH.
    caCORE: a common infrastructure for cancer informatics.
    Bioinformatics. 2003 Dec 12;19(18):2404-12. PubMed PMID: 14668224. [PubMed]

LexEVS

2016

  1. Zhao L, Lim Choi Keung SN, Arvanitis TN.
    A BioPortal-Based Terminology Service for Health Data Interoperability.
    Stud Health Technol Inform. 2016;226:143-6. PubMed PMID: 27350488. [PubMed]

    2015
  2. Delaney BC, Curcin V, Andreasson A, Arvanitis TN, Bastiaens H, Corrigan D, Ethier JF, Kostopoulou O, Kuchinke W, McGilchrist M, van Royen P, Wagner P.
    Translational Medicine and Patient Safety in Europe: TRANSFoRm--Architecture for the Learning Health System in Europe.
    Biomed Res Int. 2015;2015:961526. doi: 10.1155/2015/961526. Epub 2015 Oct 11. PubMed PMID: 26539547; PubMed Central PMCID: PMC4619923. [PubMed]
  3. Ethier JF, Curcin V, Barton A, McGilchrist MM, Bastiaens H, Andreasson A, Rossiter J, Zhao L, Arvanitis TN, Taweel A, Delaney BC, Burgun A.
    Clinical data integration model. Core interoperability ontology for research using primary care data.
    Methods Inf Med. 2015;54(1):16-23. doi: 10.3414/ME13-02-0024. Epub 2014 Jun 18. PubMed PMID: 24954896. [PubMed]

    2014
  4. Arvanitis TN.
    Semantic interoperability in healthcare.
    Stud Health Technol Inform. 2014;202:5-8. PubMed PMID: 25000001. [PubMed]

    2013
  5. Ethier JF, Dameron O, Curcin V, McGilchrist MM, Verheij RA, Arvanitis TN, Taweel A, Delaney BC, Burgun A.
    A unified structural/terminological interoperability framework based on LexEVS: application to TRANSFoRm.
    J Am Med Inform Assoc. 2013 Sep 1;20(5):986-94. doi: 10.1136/amiajnl-2012-001312. Epub 2013 Apr 9. PubMed PMID: 23571850. [PubMed]
     
    2012
  6. Lim Choi Keung SN, Zhao L, Tyler E, Hobbs FD, Arvanitis TN.
    Integrated Vocabulary Service for Health Data Interoperability.
    eTELEMED 2012, The Fourth International Conference on eHealth, Telemedicine, and Social Medicine. 2012 Jan 30:124-127. [PDF Exit Disclaimer logo ]
  7. Ouagne D, Hussain S, Sadou E, Jaulent MC, Daniel C.
    The Electronic Healthcare Record for Clinical Research (EHR4CR) information model and terminology.
    Stud Health Technol Inform. 2012;180:534-8. PubMed PMID: 22874248. [PDF Exit Disclaimer logo ]
  8. Zhao L, Lim Choi Keung SN, Taweel A, Tyler E, Ogunsina I, Rossiter J, Delaney BC, Peterson KA, Hobbs FD, Arvanitis TN.
    A Loosely Coupled Framework for Terminology Controlled Distributed EHR Search for Patient Cohort Identification in Clinical Research.
    Stud Health Technol Inform. 2012;180:519-23. PubMed PMID: 22874245. [PubMed]
     
    2011
  9. Hazen R, Van Esbroeck AP, Mongkolwat P, Channin DS.
    Automatic Extraction of Concepts to Extend RadLex.
    Journal of Digital Imaging 2011 Feb;24(1):165-169. [PDF Exit Disclaimer logo ]
  10. Salvadores M, Alexander PR, Musen MA, Noy NF.
    The Quad Economy of a Semantic Web Ontology Repository.
    The 7th International Workshop on Scalable Semantic Web Knowledge Base Systems (SSWS 2011). pp.14-29. [PDF Exit Disclaimer logo ]
     
    2010
  11. Pathak J, Peters L, Chute CG, Bodenreider O.
    Comparing and evaluating terminology services application programming interfaces: RxNav, UMLSKS and LexBIG.
    J Am Med Inform Assoc. 2010 Nov-Dec;17(6):714-9. PubMed PMID: 20962136; PubMed Central PMCID: PMC3000749. [PubMed]
     
    2009
  12. Pathak J, Solbrig HR, Buntrock JD, Johnson TM, Chute CG.
    LexGrid: a framework for representing, storing, and querying biomedical terminologies from simple to sublime.
    J Am Med Inform Assoc. 2009 May-Jun;16(3):305-15. Epub 2009 Mar 4. PubMed PMID: 19261933; PubMed Central PMCID: PMC2732233. [PubMed]
  13. Tao C, Pathak J, Solbrig HR, Chute CG.
    LexOWL: A Bridge from LexGrid to OWL.
    ICBO: International Conference on Biomedical Ontology. 2009 July 24-26. Buffalo, New York, USA.
  14. Tao C, Pathak J, Solbrig HR, Wei WQ, Chute CG.
    LexRDF Model: An RDF-based Unified Model for Heterogeneous Biomedical Ontologies.
    Proc Semantics for the Rest of Us - Variants of Semantic Web Languages in the Real World Workshop (SemRUs09). Washington DC, USA, 2009 Oct 26. PubMed PMID: 21804785; PubMed Central PMCID: PMC3146261. [PubMed] [PDF Exit Disclaimer logo ]
     
    2008
  15. Jiang G, Peterson KJ, Johnson TM, Celik C, Jakob R, Chute CG.
    Representing the ClaML-based ICD10 in LexGrid terminology model.
    AMIA Annu Symp Proc. 2008 Nov 6:992. PubMed PMID: 18998953. [PubMed]
  16. Pathak J, Jiang G, Dwarkanath SO, Buntrock JD, Chute CG.
    Adopting Graph Traversal Techniques for Context-Driven Value Sets Extraction from Biomedical Knowledge Sources.
    Proc IEEE Int Conf Semant Comput. 2008 Aug 12;2008:460-467. PubMed PMID: 21625412; PubMed Central PMCID: PMC3101576. [PubMed]
  17. Pathak J, Jiang G, Dwarkanath SO, Buntrock JD, Chute CG.
    LexValueSets: an approach for context-driven value sets extraction.
    AMIA Annu Symp Proc. 2008 Nov 6:556-60. PubMed PMID: 18998955; PubMed Central PMCID: PMC2656093. [PubMed]

Nanotechnology

  1. de la Iglesia D, García-Remesal M, Anguita A, Muñoz-Mármol M, Kulikowski C, Maojo V.
    A Machine Learning Approach to Identify Clinical Trials Involving Nanodrugs and Nanodevices from ClinicalTrials.gov.
    PLoS One. 2014 Oct 27;9(10):e110331. doi: 10.1371/journal.pone.0110331. eCollection 2014. PubMed PMID: 25347075; PubMed Central PMCID: PMC4210133. [PubMed Exit Disclaimer logo ]
  2. Klaessig FG.
    Developing official practices for nanoEHS data compilation, curation and compliance.
    Innovation and responsibility: engaging with new and emerging technologies, S.NET 005. Coenen C, Dijkstra A, Fautz C, Guivant J, Konrad K, Milburn C, van Lente H, eds. Heidelberg: IOS Press and AKA; 2014. pp. 121–133. [PDF Exit Disclaimer logo ]
  3. Zhu Z.
    Flash Nanoprecipitation: Prediction and Enhancement of Particle Stability via Drug Structure.
    Mol Pharm. 2014 Feb 3. [Epub ahead of print] PubMed PMID: 24484077. [PubMed]
  4. de la Iglesia D, Cachau RE, García-Remesal M, Maojo V.
    Nanoinformatics knowledge infrastructures: bringing efficient information management to nanomedical research.
    Comput Sci Discov. 2013 Nov 27;6(1):014011. PubMed PMID: 24932210; PubMed Central PMCID: PMC4053539. [PubMed]
  5. Thomas DG, Gaheen S, Harper SL, Fritts M, Klaessig F, Hahn-Dantona E, Paik D, Pan S, Stafford GA, Freund ET, Klemm JD, Baker NA.
    ISA-TAB-Nano: a specification for sharing nanomaterial research data in spreadsheet-based format.
    BMC Biotechnol. 2013 Jan 14;13:2. doi: 10.1186/1472-6750-13-2. PubMed PMID: 23311978;
    PubMed Central PMCID: PMC3598649. [PubMed]
  6. Thomas DG, Klaessig F, Harper SL, Fritts M, Hoover MD, Gaheen S, Stokes TH, Reznik-Zellen R, Freund ET, Klemm JD, Paik DS, Baker NA.
    Informatics and standards for nanomedicine technology.
    Wiley Interdiscip Rev Nanomed Nanobiotechnol. 2011 Sept/Oct 3(5):511–532. PubMed PMID: 21721140; PubMed Central PMCID: PMC3189420. [PubMed]
  7. Thomas DG, Pappu RV, Baker NA.
    NanoParticle Ontology for cancer nanotechnology research.
    J Biomed Inform. 2011 Feb;44(1):59-74. Epub 2010 Mar 6. PubMed PMID: 20211274; PubMed Central PMCID: PMC3042056. [PubMed]
  8. Bailey LO, Kennedy CH, Fritts MJ, Hartel FW.
    Development of a model for the representation of nanotechnology-specific terminology.
    AMIA Annu Symp Proc. 2006:849. PubMed PMID: 17238469; PubMed Central PMCID: PMC1839578. [PubMed]

NLP, Ontology, Semantics, Tools


2018

  1. Karimi H, Kamandi A. Ontology alignment using inductive logic programming
    Conference: 2018 4th International Conference on Web Research (ICWR) DOI: 10.1109/ICWR.2018.8387247, April 2018

2017

  1. Nikiema JN, Jouhet, Mougin F.
    Integrating cancer diagnosis terminologies based on logical definitions of SNOMED CT concepts.
    J Biomed Inform. 2017 Oct;74:46-58. doi: 10.1016/j.jbi.2017.08.013. Epub 2017 Aug 24. [PubMed]
  2. Min H, Zheng L, Perl Y, Halper M, De Coronado S, Ochs C.
    Relating Complexity and Error Rates of Ontology Concepts. More Complex NCIt Concepts Have More Errors.
    Methods Inf Med. 2017 May 18;56(3):200-208. doi: 10.3414/ME16-01-0085. Epub 2017 Feb 28. [PubMed]

2016

  1. Bamparopoulos G, Konstantinidis E, Bratsas C, Bamidis PD.
    Towards exergaming commons: composing the exergame ontology for publishing open game data.
    J Biomed Semantics. 2016 Feb 9;7:4. doi: 10.1186/s13326-016-0046-4. eCollection 2016. PubMed PMID: 26865947; PubMed Central PMCID: PMC4748514. [PubMed]
  2. Chang M, Chang M, Reed JZ, Milward D, Xu JJ, Cornell WD.
    Developing timely insights into comparative effectiveness research with a text-mining pipeline.
    Drug Discov Today. 2016 Feb 6. pii: S1359-6446(16)00032-5. doi: 10.1016/j.drudis.2016.01.012. [Epub ahead of print] Review. PubMed PMID: 26854423. [PubMed]
  3. Daniel C, Ouagne D, Sadou E, Forsberg K, Gilchrist MM, Zapletal E, Paris N, Hussain S, Jaulent MC, Md DK.
    Cross border semantic interoperability for clinical research: the EHR4CR semantic resources and services.
    AMIA Jt Summits Transl Sci Proc. 2016 Jul 20;2016:51-9. PubMed PMID: 27570649; PubMed Central PMCID: PMC5001763. [PubMed]
  4. Groß A, Pruski C, Rahm E.
    Evolution of biomedical ontologies and mappings: Overview of recent approaches.
    Comput Struct Biotechnol J. 2016 Aug 26;14:333-40. doi: 10.1016/j.csbj.2016.08.002. Review. PubMed PMID: 27642503; PubMed Central PMCID: PMC5018063. [PubMed]
  5. He Z, Geller J.
    Preliminary Analysis of Difficulty of Importing Pattern-Based Concepts into the National Cancer Institute Thesaurus.
    Stud Health Technol Inform. 2016;228:389-93. PubMed PMID: 27577410. [PubMed]
  6. McEntire R, Szalkowski D, Butler J, Kuo MS, Chang M, Chang M, Freeman D, McQuay S, Patel J, McGlashen M, Cornell WD, Xu JJ.
    Application of an automated natural language processing (NLP) workflow to enable federated search of external biomedical content in drug discovery and development.
    Drug Discov Today. 2016 May;21(5):826-35. doi: 10.1016/j.drudis.2016.03.006. Review. PubMed PMID: 26979546. [PubMed]
  7. Min H, Turner S, de Coronado S, Davis B, Whetzel T, Freimuth RR, Solbrig HR, Kiefer R, Riben M, Stafford GA, Wright LW, Ohira R.
    Towards a Standard Ontology Metadata Model.
    Proc International Conference on Biomedical Ontology 2016 (ICBO 2016). Corvallis, Oregon, USA, 1-4 Aug 2016. [PDF Exit Disclaimer logo ]
  8. Ochs C, Geller J, Perl Y, Musen MA.
    A unified software framework for deriving, visualizing, and exploring abstraction networks for ontologies.
    J Biomed Inform. 2016 Aug;62:90-105. doi: 10.1016/j.jbi.2016.06.008. PubMed PMID: 27345947; PubMed Central PMCID: PMC4987206. [PubMed]
  9. Queralt-Rosinach N, Piñero J, Bravo À, Sanz F, Furlong LI.
    DisGeNET-RDF: harnessing the innovative power of the Semantic Web to explore the genetic basis of diseases.
    Bioinformatics. 2016 Jul 15;32(14):2236-8. doi:10.1093/bioinformatics/btw214. Epub 2016 Apr 22. PubMed PMID: 27153650; PubMed Central PMCID: PMC4937199. [PubMed]
  10. Slater L, Gkoutos GV, Schofield PN, Hoehndorf R.
    Using AberOWL for fast and scalable reasoning over BioPortal ontologies.
    J Biomed Semantics. 2016 Aug 8;7(1):49. doi: 10.1186/s13326-016-0090-0. PubMed PMID: 27502585; PubMed Central PMCID: PMC4976511. [PubMed]
  11. Zulkarnain NZ, Meziane F, Crofts G.
    A methodology for biomedical ontology reuse.
    21st Internat Conf on Applications of Natural Language to Information Systems (NLDB 2016). Salford, UK, 22-24 June 2016.

    2015
  12. Amin MB, Khan WA, Kang BH, Lee S.
    Performance-based Ontology Matching, A Data-parallel approach for an Effectiveness-independent Performance-gain in Ontology Matching.
    Applied Intelligence. 2015 Sept, 43(2):356-385. [PDF Exit Disclaimer logo ]
  13. Araújo TB, Pires CE, Nobrega TP, Nascimento DC.
    A Parallel Approach for Matching Large-scale Ontologies.
    J of Information and Data Management. 6(1):18-31 Feb 2015.[PDF Exit Disclaimer logo ]
  14. Bekhuis T, Tseytlin E, Mitchell KJ.
    A Prototype for a Hybrid System to Support Systematic Review Teams: A Case Study of Organ Transplantation.
    2015 Intl Workshop on Biomedical and Health Informatics at the IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 2015, Bethesda, MD, US, Nov 9-12, 2015.[PDF Exit Disclaimer logo ]
  15. Chen ES, Sarkar IN.
    *informatics: Identifying and Tracking Informatics Sub-Discipline Terms in the Literature.
    Methods Inf Med. 2015 Nov 27;54(6):530-9. doi: 10.3414/ME14-01-0088. Epub 2015 May 22. PubMed PMID: 25998007.[PubMed]
  16. Chen J, Ludwig M, Ma Y, Walther D.
    Towards Extracting Ontology Excerpts.
    Procs 8th Intl Conf Knowledge Science, Engineering and Management (KSEM 2015). Chongqing, China, Oct 28-30, 2015, pp.78-89.
  17. Christen V, Hartung M, Groß A.
    Region Evolution eXplorer - A tool for discovering evolution trends in ontology regions.
    J Biomed Semantics. 2015 Jun 1;6:26. doi: 10.1186/s13326-015-0020-6. eCollection 2015. PubMed PMID: 26034559; PubMed Central PMCID: PMC4450457. [PubMed]
  18. Cormack J, Nath C, Milward D, Raja K, Jonnalagadda SR.
    Agile Text Mining for the 2014 i2b2/UTHealth Cardiac Risk Factors Challenge.
    J Biomed Inform. 2015 Jul 21. pii: S1532-0464(15)00141-0. doi: 10.1016/j.jbi.2015.06.030. [Epub ahead of print] PubMed PMID: 26209007. [PubMed]
  19. Dos Reis JC, Pruski C, Da Silveira M, Reynaud-Delaître C.
    DyKOSMap: A framework for mapping adaptation between biomedical knowledge organization systems.
    J Biomed Inform. 2015 Apr 15. pii: S1532-0464(15)00068-4. doi:10.1016/j.jbi.2015.04.001. [Epub ahead of print] PubMed PMID: 25889690. [PubMed]
  20. Halper M, Gu H, Perl Y, Ochs C.
    Abstraction networks for terminologies: Supporting management of "big knowledge".
    Artif Intell Med. 2015 May;64(1):1-16. doi: 10.1016/j.artmed.2015.03.005. Epub 2015 Apr 2. Review. PubMed PMID: 25890687. [PubMed]
  21. Jin R, You J, Chung JW, Lee HJ, Wolters M, Park JC.
    CoMAGD: Annotation of Gene-Depression Relations.
    Proc 2015 Workshop on Biomedical Natural Language Processing (BioNLP 2015). Beijing, China, July 30, 2015, pp.104–113, Association for Computational Linguistics. [PDF Exit Disclaimer logo ]
  22. Kalet AM.
    Bayesian networks from ontological formalisms in radiation oncology.
    Thesis (Ph.D.) University of Washington, 2015. [PDF Exit Disclaimer logo ]
  23. Lambrix P, Wei-Kleiner F, Dragisic Z.
    Completing the is-a structure in light-weight ontologies.
    J Biomed Semantics. 2015 Mar 28;6:12. doi:10.1186/s13326-015-0002-8. eCollection 2015. PubMed PMID: 25883780; PubMed Central PMCID: PMC4399482. [PubMed]
  24. Martins C, Jimenez-Ruiz E, Santos E, Pesquita C.
    Towards visualizing the mapping incoherences in Bioportal.
    Proc International Conference on Biomedical Ontology 2015 (ICBO 2015). Lisbon, Portugal, 27-30 July 2015. [PDF Exit Disclaimer logo ]
  25. Matos S, Campos D, Pinho R, Silva R, Mort M, Cooper DN, Oliveira JL.
    Assisted Mining and Curation of Genomic Variants using Egas.
    Proc Fifth BioCreative Challenge Evaluation Workshop. Sept 9, 2015, Sevilla, Spain, pp.396-402. [PDF Exit Disclaimer logo ]
  26. Möller MB.
    Connecting GOMMA with STROMA: An Approach for Semantic Ontology Mapping in the Biomedical Domain.
    Thesis (B.Sc.) Leipzig University, 2015. [PDF Exit Disclaimer logo ]
  27. Otero-Cerdeira L, Rodríguez-Martínez FJ, Gómez-Rodríguez A.
    Ontology matching: A literature review.
    Expert Systems with Applications. 42(2):949–971, 1 Feb 2015, Epub 30 Aug 2014, ISSN 0957-4174, http://dx.doi.org/10.1016/j.eswa.2014.08.032. [ScienceDirect Exit Disclaimer logo ]
  28. Palma G, Vidal ME, Haag E, Raschid L, Thor A.
    Determining similarity of scientific entities in annotation datasets.
    Database (Oxford). 2015 Feb 27;2015. pii: bau123. doi: 10.1093/database/bau123. Print 2015. PubMed PMID: 25725057; PubMed Central PMCID: PMC4343076. [PubMed]
  29. Panov P, Soldatova LN, Džeroski S.
    Generic Ontology of Datatypes.
    Information Sciences. 2015 Aug 9 (online, in press). doi: 10.1016/j.ins.2015.08.006.
  30. Piñero J, Queralt-Rosinach N, Bravo À, Deu-Pons J, Bauer-Mehren A, Baron M, Sanz F, Furlong LI.
    DisGeNET: a discovery platform for the dynamical exploration of human diseases and their genes.
    Database (Oxford). 2015 Apr 15;2015:bav028. doi: 10.1093/database/bav028. Print 2015. PubMed PMID: 25877637; PubMed Central PMCID: PMC4397996. [PubMed]
  31. Steigmiller A Glimm B.
    Pay-As-You-Go Description Logic Reasoning by Coupling Tableau and Saturation Procedures
    J of Artificial Intelligence Research. 2015 Dec 15; 54:535-592.[PDF Exit Disclaimer logo ]
  32. Trivela D, Stoilos G, Chortaras A, Stamou G.
    Optimising resolution-based rewriting algorithms for OWL ontologies.
    Web Semantics: Science, Services and Agents on the World Wide Web. Online 26 Feb 2015. [Online Exit Disclaimer logo ]
  33. Wang Z, Wang K, Topor R.
    DL-Lite Ontology Revision Based on An Alternative Semantic Characterisation.
    ACM Transactions on Computational Logic (TOCL). 2015 (Accepted). [PDF Exit Disclaimer logo ]

    2014
  34. Ashish N, Dahm L, Boicey C.
    University of California, Irvine-Pathology Extraction Pipeline: The pathology extraction pipeline for information extraction from pathology reports.
    Health Informatics J. 2014 Dec;20(4):288-305. doi: 10.1177/1460458213494032. Epub 2014 Aug 25. PubMed PMID: 25155030. [PubMed Exit Disclaimer logo ]
  35. Blair DR, Wang K, Nestorov S, Evans JA, Rzhetsky A.
    Quantifying the impact and extent of undocumented biomedical synonymy.
    PLoS Comput Biol. 2014 Sep 25;10(9):e1003799. doi: 10.1371/journal.pcbi.1003799. eCollection 2014 Sep. PubMed PMID: 25255227; PubMed Central PMCID: PMC4177665. [PubMed Exit Disclaimer logo ]
  36. Brochhausen M, Schneider J, Malone D, Empey PE, Hogan WR, Boyce RD.
    Towards a foundational representation of potential drug-drug interaction knowledge.
    First International Workshop on Drug Interaction Knowledge Representation (DIKR-2014) at the International Conference on Biomedical Ontologies (ICBO 2014). Houston, Texas, USA, Oct 6, 2014. [PDF Exit Disclaimer logo ]
  37. Djeddi WE, Khadir MT.
    XMap++ : Results for OAEI 2014.
    Ninth International Workshop on Ontology Matching (OM-2014) collocated with the 13th International Semantic Web Conference (ISWC-2014). Oct 20, 2014, Trentino, Italy. [PDF Exit Disclaimer logo ]
  38. Dos Reis JC.
    Mapping Adaptation between Biomedical Knowledge Organization Systems.
    Thesis (Ph.D.) Université de Paris-Sud, France, Oct 24, 2014. [PDF Exit Disclaimer logo ]
  39. Dragisic Z, Eckert K, Euzenat J, Faria D, Ferrara A, Granada R, Ivanova V, Jiménez-Ruiz E, Kempf AO, Lambrix P, Montanelli S, Paulheim H, Ritze D, Shvaiko P, Solimando A, Trojahn C, Zamazal O, Cuenca Grau B.
    Results of the Ontology Alignment Evaluation Initiative 2014.
    Proc 10th International Workshop on Ontology Matching(OM-2014), co-located with the 12th International Semantic Web Conference (ISWC 2014), Riva del Garda, Italy, 2014. [PDF Exit Disclaimer logo ]
  40. Faria D, Jiménez-Ruiz E, Pesquita C, Santos E, Couto FM.
    Towards annotating potential incoherences in BioPortal mappings.
    Proc 13th International Semantic Web Conference (ISWC-2014). Oct 19-23, 2014, Trentino, Italy, pp.17-32. [PDF Exit Disclaimer logo ]
  41. Faria D, Pesquita C, Santos E, Cruz IF, Couto FM.
    Automatic background knowledge selection for matching biomedical ontologies.
    PLoS One. 2014 Nov 7;9(11):e111226. doi: 10.1371/journal.pone.0111226. eCollection 2014. PubMed PMID: 25379899. [PubMed]
  42. Fu X, Batista-Navarro R, Rak R, Ananiadou S.
    A Strategy for Annotating Clinical Records with Phenotypic Information relating to the Chronic Obstructive Pulmonary Disease.
    Proc Phenotype Day at ISMB 2014. Boston, MA, USA, July 12, 2014. [PDF Exit Disclaimer logo ]
  43. Gatens W, Konev B, Wolter F.
    Lower and Upper Approximations for Depleting Modules of Description Logic Ontologies.
    Proc 21st European Conference on Artificial Intelligence (ECAI 2014). Aug 18-22, 2014, Prague, Czech Republic. [PDF Exit Disclaimer logo ]
  44. Gonçalves JRLS.
    Impact Analysis in Description Logic Ontologies.
    Thesis (Ph.D.) Faculty of Engineering and Physical Sciences, University of Manchester, 2014. [PDF Exit Disclaimer logo ]
  45. Gupta S, MacLean DL, Heer J, Manning CD.
    Induced lexico-syntactic patterns improve information extraction from online medical forums.
    J Am Med Inform Assoc. 2014 Sep-Oct;21(5):902-9. doi: 10.1136/amiajnl-2014-002669. Epub 2014 Jun 26. PubMed PMID: 24970840; PubMed Central PMCID: PMC4147618. [PubMed Exit Disclaimer logo ]
  46. He Z, Geller J, Elhanan G.
    Categorizing the Relationships between Structurally Congruent Concepts from Pairs of Terminologies for Semantic Harmonization.
    Proc 2014 Summit on Translational Bioinformatics (TBI 2014). April 7-9, 2014, San Francisco, CA, pp.48-53.
  47. Hudson CL, Topaloglu U, Bian J, Hogan W, Kieber-Emmons T.
    Automated Tools for Clinical Research Data Quality Control using NCI Common Data Elements.
    Proc 2014 Summit on Translational Bioinformatics (TBI 2014). April 7-9, 2014, San Francisco, CA, pp.60-69.
  48. Ivanova V.
    Integration of Ontology Alignment and Ontology Debugging for Taxonomy Networks.
    Thesis (Licentiate) Linköping University, Department of Computer and Information Science, National Graduate School of Computer Science, Linköping, Sweden, 2014. [PDF Exit Disclaimer logo ]
  49. Ivanović M, Budimac Z.
    An overview of ontologies and data resources in medical domains.
    Expert Systems with Applications 2014 Sept 1; 41(11):5158-5166, ISSN 0957-4174. [DOI Exit Disclaimer logo ]
  50. Jiang G, Sharma DK, Solbrig HR, Tao C, Weng C, Chute CG.
    Building a Semantic Web-based Metadata Repository for Facilitating Detailed Clinical Modeling in Cancer Genome Studies.
    Proc 7th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2014). Berlin, Germany, Dec 9-11, 2014. [PDF Exit Disclaimer logo ]
  51. Loebe F, Chaudhri VK.
    Entities with Genetic Information – An Initial Perspective from the Core Theme of Continuity and Change in Biology.
    Ontologies and Data in Life Sciences (ODLS 2014). Freiburg im Breisgau, October 7-8, 2014. [PDF Exit Disclaimer logo ]
  52. Ludwig M.
    Just: a Tool for Computing Justifications w.r.t. ELH Ontologies.
    Proc 3rd International Workshop on OWL Reasoner Evaluation (ORE 2014). Vienna, Austria, July 13, 2014, pp.1-7. [PDF Exit Disclaimer logo ]
  53. Ludwig M, Konev B.
    Practical Uniform Interpolation and Forgetting for ALC TBoxes with Applications to Logical Difference.
    Proc 14th International Conference on Principles of Knowledge Representation and Reasoning (KR 2014). Vienna, Austria, July 20-24, 2014. [PDF Exit Disclaimer logo ]
  54. Ludwig M, Peñaloza R.
    Brave and Cautious Reasoning in EL.
    Proc 27th International Workshop on Description Logics (DL 2014). Vienna, Austria, July 17-20, 2014, pp.274-286. [PDF Exit Disclaimer logo ]
  55. Ludwig M, Peñaloza R.
    Error-Tolerant Reasoning in the Description Logic EL.
    Proceedings of the 14th European Conference on Logics in Artificial Intelligence (JELIA 2014). Sept 24-26, 2014,Madeira, Portugal. [PDF Exit Disclaimer logo ]
  56. Martínez-Romero M, Vázquez-Naya JM, Pereira J, Pazos A.
    BiOSS: A system for biomedical ontology selection.
    Computer Methods and Programs in Biomedicine, 2014 Apr;114(1):125-40. doi: 10.1016/j.cmpb.2014.01.020. Epub 2014 Feb 6. PubMed PMID: 24573129. [PubMed]
  57. Merrill E, Corlosquet S, Ciccarese P, Clark T, Das S.
    Semantic Web repositories for genomics data using the eXframe platform.
    J Biomed Semantics. 2014 Jun 3;5(Suppl 1 Proceedings of the Bio-Ontologies Spec Interest G):S3. doi: 10.1186/2041-1480-5-S1-S3. eCollection 2014. PubMed PMID: 25093072; PubMed Central PMCID: PMC4108874. [PubMed]
  58. Mortensen JM, Minty EP, Januszyk M, Sweeney TE, Rector AL, Noy NF, Musen MA.
    Using the wisdom of the crowds to find critical errors in biomedical ontologies: a study of SNOMED CT.
    J Am Med Inform Assoc. 2014 Oct 23. pii: amiajnl-2014-002901. doi: 10.1136/amiajnl-2014-002901. [Epub ahead of print] PubMed PMID: 25342179. [PubMed Exit Disclaimer logo ]
  59. Névéol A, Grosjean J, Darmoni S, Zweigenbaum P.
    Language Resources for French in the Biomedical Domain.
    Proceedings of the Ninth International Conference on Language Resources and Evaluation (LREC 2014). Reykjavik, Iceland, May 26-31, 2014. [PDF Exit Disclaimer logo ]
  60. Pesquita C, Ferreira JD, Couto FM, Silva MJ.
    The epidemiology ontology: an ontology for the semantic annotation of epidemiological resources.
    J Biomed Semantics. 2014 Jan 17;5(1):4. doi: 10.1186/2041-1480-5-4. PubMed PMID: 24438387; PubMed Central PMCID: PMC3926306. [PubMed]
  61. Queral-Rosinach N, Kuhn T, Chichester C, Dumontier M, Sanz F, Furlong LI.
    Publishing DisGeNET as Nanopublications.
    Semantic Web – Interoperability, Usability, Applicability (Semantic Web journal). 2014 Oct 15. [PDF Exit Disclaimer logo ]
  62. Raunich S, Rahm E.
    Target-driven merging of taxonomies with ATOM.
    Information Systems. 2014 June; 42(June):1-14. ISSN 0306-4379. [DOI] [URL Exit Disclaimer logo ]
  63. Reyes L, Molina D, Hidalgo Y, Roldán MM, Aldana JF.
    Exploring Incremental Reasoning Approaches based on Module Extraction.
    Proceedings of the 1st Cuban Workshop on Semantic Web 2014 (TCWS 2014) co-located with 13th International Congress on Information (INFO 2014). Havana, Cuba, Apr 16, 2014, pp.1-12. [PDF Exit Disclaimer logo ]
  64. Shen G, Liu Y, Wang F, Si J, Wang Z, Huang Z, Kang D.
    OMReasoner: combination of multi-matchers for ontology matching: results for OAEI 2014.
    Ninth International Workshop on Ontology Matching (OM-2014) collocated with the 13th International Semantic Web Conference (ISWC-2014). Oct 20, 2014, Trentino, Italy. [PDF Exit Disclaimer logo ]
  65. Shivade C, Cormack J, Milward D.
    Precise Medication Extraction using Agile Text Mining.
    Proceedings of the 5th International Workshop on Health Text Mining and Information Analysis (Louhi). 2014 April, Gothenburg, Sweden, Association for Computational Linguistics pp.75-79. [PDF Exit Disclaimer logo ]
  66. Silva MAA, Cavalcanti MC.
    Combining Ontology Modules for Scientific Text Annotation.
    Journal of Information and Data Management. 5(3):238-251, 2014. [PDF Exit Disclaimer logo ]
  67. Spasić I, Livsey J, Keane JA, Nenadić G.
    Text mining of cancer-related information: Review of current status and future directions.
    Int J Med Inform. 2014 Sep;83(9):605-623. doi: 10.1016/j.ijmedinf.2014.06.009. Epub 2014 Jun 24. Review. PubMed PMID: 25008281. [PubMed]
  68. Tao S.
    An Ontology-Driven Interface for Computable Modeling of Clinical Trial Eligibility Criteria.
    Thesis (M.S.), Case Western Reserve University, EECS - Computer and Information Sciences, 17 Mar 2014.
    [OhioLINK Exit Disclaimer logo ]
  69. Walk S, Singer P, Strohmaier M.
    Sequential Action Patterns in Collaborative Ontology-Engineering Projects: A Case-Study in the Biomedical Domain.
    Proceedings of the 23rd ACM International Conference on Information and Knowledge Management (CIKM 2014). Nov 3-7, 2014 Shanghai, China. [PDF Exit Disclaimer logo ]
  70. Walk S, Singer P, Strohmaier M, Tudorache T, Musen MA, Noy NF.
    Discovering Beaten Paths in Collaborative Ontology-Engineering Projects using Markov Chains.
    J Biomed Inform. 2014 Oct;51:254-71. doi: 10.1016/j.jbi.2014.06.004. Epub 2014 Jun 17. PubMed PMID: 24953242; PubMed Central PMCID: PMC4194274.  [PubMed Exit Disclaimer logo ]
  71. Wu Q, Zhang H, Hong Q, Zheng Y, Wu M.
    Bursty topic detection based on K-states automaton model: An emerging trend in cancer field.
    Proceedings of the 6th International Conference on Bioinformatics and Computational Biology (BICoB 2014). 24-26 March 2014, Las Vegas, Nevada, USA, pp. 125-130.
  72. Wu Q, Zhang H, Lan J.
    K-State automaton burst detection model based on KOS: Emerging trends in cancer field.
    Journal of Information Science. Oct 3, 2014. doi: 10.1177/0165551514551500. [Online Exit Disclaimer logo ]
  73. Zese R, Bellodi E, Lamma E, Riguzzi F, Aguiari F.
    Semantics and Inference for Probabilistic Description Logics.
    10th International Workshop on Uncertainty Reasoning for the Semantic Web (URSW 2014), collocated with the 13th International Semantic Web Conference (ISWC-2014). Oct 20, 2014, Trentino, Italy. [PDF Exit Disclaimer logo ]

    2013
  74. Amin MB, Batool R, Khan WA, Lee S, Huh EN.
    SPHeRe: A Performance Initiative Towards Ontology Matching by Implementing Parallelism over Cloud Platform.
    Journal of Supercomputing. October 2013. [PDF Exit Disclaimer logo ] [Springer Exit Disclaimer logo ]
  75. Armas Romero A, Grau BC, Horrocks I, Jiménez-Ruiz E.
    MORe: a Modular OWL Reasoner for Ontology Classification.
    Proceedings of the 2nd OWL Reasoner Evaluation Workshop (ORE 2013), Ulm, Germany, July 22, 2013. [PDF Exit Disclaimer logo ]
  76. Ba M, Diallo G.
    Large-scale biomedical ontology matching with ServOMap.
    IRBM, 2013. 34(1):56-59. Available online 13 February 2013. [DOI Exit Disclaimer logo ]
  77. Beißwanger AE.
    Developing Ontological Background Knowledge for Biomedicine.
    Thesis (Doktors der Naturwissenschaften), University of Mannheim, Mannheim, 2013. [PDF Exit Disclaimer logo ]
  78. Cross V, Yu X, Hu X.
    Unifying ontological similarity measures: A theoretical and empirical investigation.
    International Journal of Approximate Reasoning. 54(7):861 - 875 Sept 2013. [Full Text Exit Disclaimer logo ]
  79. Cuenca Grau B, Dragisic Z, Eckert K, Euzenat J, Ferrara A, Granada R, Ivanova V, Jiménez-Ruiz E, Kempf AO, Lambrix P, Nikolov A, Paulheim H, Ritze D, Scharffe F, Shvaiko P, Trojahn C, Zamazal O.
    Results of the Ontology Alignment Evaluation Initiative 2013.
    Proceedings of the 8th International Workshop on Ontology Matching (OM-2013), co-located with the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, pp.61-100, October 21, 2013. [PDF Exit Disclaimer logo ]
  80. Dos Reis JC, Dinh D, Pruski C, Da Silveira M, Reynaud-Delaître C.
    Mapping Adaptation Actions for the Automatic Reconciliation of Dynamic Ontologies.
    Proceedings of the 22nd ACM International Conference on Information and Knowledge Management (CIKM '13) Oct 27 - Nov 1, 2013, San Francisco, California, USA. ACM, New York, NY, USA, 599-608. DOI=10.1145/2505515.2505564 [ACM Exit Disclaimer logo ]
  81. Freckleton RE.
    Scaling Ontology Alignment.
    Thesis (M.S.), University of Colorado Colorado Springs, Department of Computer Science, 2013. [PDF Exit Disclaimer logo ]
  82. Garcia Castro LJ, McLaughlin C, Garcia A.
    Biotea: RDFizing PubMed Central in support for the paper as an interface to the Web of Data.
    J Biomed Semantics. 2013 Apr 15;4 Suppl 1:S5. doi: 10.1186/2041-1480-4-S1-S5. Epub 2013 Apr 15. PubMed PMID: 23734622. [PubMed]
  83. Gonçalves RS, Bail S, Jimenez-Ruiz E, Matentzoglu N, Parsia B, Glimm B, Kazakov Y.
    OWL Reasoner Evaluation (ORE) Workshop 2013 Results: Short Report.
    Proceedings of the 2nd OWL Reasoner Evaluation Workshop (ORE 2013), Ulm, Germany, July 22, 2013 pp.1-18. [PDF Exit Disclaimer logo ]
  84. Gonçalves RS, Matentzoglu N, Parsia B, Sattler U.
    The Empirical Robustness of Description Logic Classification.
    Proceedings of the 26th International Workshop on Description Logics (DL 2013), Ulm, Germany, July 23-26, 2013 (in CEUR Workshop Proceedings. CEUR-WS.org, July 2013. [PDF Exit Disclaimer logo ]
  85. Gross A, Dos Reis JC, Hartung M, Pruski C, Rahm E.
    Semi-Automatic Adaptation of Mappings between Life Science Ontologies.
    Proc. 9th Intl. Conference on Data Integration in the Life Sciences (DILS), 2013 (to appear) July 11-12, 2013, Montreal, Quebec, Canada. [PDF Exit Disclaimer logo ]
  86. Hartung M, Gross A, Rahm E.
    Composition Methods for Link Discovery.
    Proc. of 15. GI-Fachtagung Datenbanksysteme für Business, Technologie und Web (BTW 2013). March 11-15, 2013, Magdeburg, Germany. [Leipzig Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  87. Hartung M, Gross A, Rahm E.
    COnto-Diff: Generation of Complex Evolution Mappings for Life Science Ontologies.
    J Biomed Inform. 2013 Feb;46(1):15-32. doi:10.1016/j.jbi.2012.04.009. Epub 2012 May 8. PubMed PMID: 22580476. [PubMed]
  88. Hartung M, Kolb L, Gross A, Rahm E.
    Optimizing Similarity Computations for Ontology Matching - Experiences from GOMMA.
    Proc. 9th Intl. Conference on Data Integration in the Life Sciences (DILS), 2013 (to appear) July 11-12, 2013, Montreal, Quebec, Canada. [PDF Exit Disclaimer logo ]
  89. Hedeler C, Parsia B, Brandt S.
    Estimating Coordination in Medical Terminologies.
    Proceedings of the 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2013). Edinburgh, UK, December 10, 2013. [PDF Exit Disclaimer logo ]
  90. Karaiskos C.
    Enhanced Ontological Searching of Medical Scientific Information.
    Thesis (M.Sc.), University of Manchester, School of Computer Science, 2013. [PDF Exit Disclaimer logo ]
  91. Kocbek S, Kim JD, Perret JL, Whetzel PL.
    Visualizing ontology mappings to help ontology engineers identify relevant ontologies for their reuse.
    Proc of the 4th International Conference on Biomedical Ontology (ICBO 2013), Montreal, Quebec, Canada, July 8-9, 2013. [PDF Exit Disclaimer logo ]
  92. Kozák J, Nečaský M, Dědek J, Klímek J, Pokorný J.
    Linked Open Data for Healthcare Professionals.
    Proceedings of International Conference on Information Integration and Web-based Applications and Services (IIWAS '13), 2013. ACM, New York, NY, USA. DOI=10.1145/2539150.2539195 [Online Exit Disclaimer logo ]
  93. Kozák J, Necaský M, Dedek J, Klímek J, Pokorný J.
    Using Linked Data for Better Navigation in Summaries of Product Characteristics.
    Proceedings of the 6th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2013). Edinburgh, UK, December 10, 2013. [PDF Exit Disclaimer logo ]
  94. Lambrix P, Liu Q.
    Debugging the missing is-a structure within taxonomies networked by partial reference alignments.
    Data & Knowledge Engineering, in press, available online 29 March 2013, ISSN 0169-023X, 10.1016/j.datak.2013.03.003. [Online Exit Disclaimer logo ]
  95. Lim L, Wang H, Wang M.
    Semantic Queries by Example.
    Proceedings of the 16th International Conference on Extending Database Technology (EDBT 2013) Genoa, Italy, 2013 Mar 18-22:347-358. ACM New York, NY, USA, 2013. [PDF Exit Disclaimer logo ] [ACM DL Exit Disclaimer logo ]
  96. Ludwig M, Konev B.
    Towards Practical Uniform Interpolation and Forgetting for ALC TBoxes.
    Proceedings of the 26th International Workshop on Description Logics (DL 2013), Ulm, Germany, July 23-26, 2013 (in CEUR Workshop Proceedings. CEUR-WS.org, July 2013. [PDF Exit Disclaimer logo ]
  97. Matentzoglu N, Bail S, Parsia B.
    A Corpus of OWL DL Ontologies.
    Proceedings of the 26th International Workshop on Description Logics (DL 2013), Ulm, Germany, July 23-26, 2013 (in CEUR Workshop Proceedings. CEUR-WS.org, July 2013. [PDF Exit Disclaimer logo ]
  98. Milian K, Bucur A, van Harmelen F, ten Teije A.
    Identifying Most Relevant Concepts to Describe Clinical Trial Eligibility Criteria.
    Proc 6th International Conference on Health Informatics (HEALTHINF 2013), 13 Feb 2013, Barcelona Spain. [PDF Exit Disclaimer logo ]
  99. Mudunuri US, Khouja M, Repetski S, Venkataraman G, Che A, Luke BT, Girard FP, Stephens RM.
    Knowledge and Theme Discovery across Very Large Biological Data Sets Using Distributed Queries: A Prototype Combining Unstructured and Structured Data.
    PLoS One. 2013 Dec 2;8(12):e80503. doi: 10.1371/journal.pone.0080503. eCollection 2013. PubMed PMID: 24312478; PubMed Central PMCID: PMC3846626. [PubMed]
  100. Nikitina N, Schewe S.
    Simplifying Description Logic Ontologies.
    Proceedings of the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, Oct 21-25, 2013. [ISWC Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  101. Noy NF, Alexander PR, Harpaz R, Whetzel PL, Fergerson RW, Musen MA.
    Getting Lucky in Ontology Search: A Data-Driven Evaluation Framework for Ontology Ranking.
    Proceedings of the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, 21-25 Oct 2013. [PDF Exit Disclaimer logo ]
  102. Palma G, Vidal ME, Haag E, Raschid L, Thor A.
    Measuring Relatedness Between Scientific Entities in Annotation Datasets.
    ACM Conference on Bioinformatics, Computational Biology and Biomedical Informatics (ACM BCB 2013) Bethesda, MD, 2013 Sept 22-25. [PDF Exit Disclaimer logo ]
  103. Palma G, Vidal ME, Raschid L, Thor A.
    Exploiting Semantics from Ontologies and Shared Annotations to Find Patterns in Annotated Linked Open Data.
    3rd International Workshop on Linked Science 2013 (LISC 2013), in conjunction with International Semantic Web Conference (ISWC 2013), Oct 21-22 2013, Sydney, Australia. [PDF Exit Disclaimer logo ]
  104. Pesquita C, Faria F, Santos E, Couto FM.
    To Repair or Not To Repair: Reconciling Correctness and Coherence in Ontology Reference Alignments.
    Proceedings of the 8th International Workshop on Ontology Matching (OM-2013), co-located with the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, pp.13-24, October 21, 2013. [PDF Exit Disclaimer logo ]
  105. Salvadores M, Alexander PR, Musen MA, Noy NF.
    BioPortal as a Dataset of Linked Biomedical Ontologies and Terminologies in RDF.
    Semantic Web Journal 2013; 4(3):277-284. Available online 31 Oct 2012. [PDF Exit Disclaimer logo ] [Online Exit Disclaimer logo ]
  106. Shvaiko P, Euzenat J, Srinivas K, Mao M, Jiménez-Ruiz E, eds.
    Ontology Matching (OM-2013).
    Proceedings of the 8th International Workshop on Ontology Matching (OM-2013), co-located with the 12th International Semantic Web Conference (ISWC 2013), Sydney, Australia, pp.101-108, October 21, 2013. [CEUR-WS Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  107. Sojic A.
    Biomedical Ontologies: Examining Aspects of Integration Across Breast Cancer Knowledge Domains.
    Thesis (Ph.D.) European School of Molecular Medicine (SEMM) and University of Milan Faculty of Medicine, 2013. [PDF Exit Disclaimer logo ]
  108. Strohmaier M, Walk S, Pöschko J, Lamprecht D, Tudorache T, Nyulas C, Musen MA, Noy NF.
    How Ontologies are Made: Studying the Hidden Social Dynamics Behind Collaborative Ontology Engineering Projects.
    Web Semantics: Science, Services and Agents on the World Wide Web, North America, April 2013. [PDF Exit Disclaimer logo ]
  109. Tao C, Pathak J, Solbrig HR, Wei WQ, Chute CG.
    Terminology Representation Guidelines for Biomedical Ontologies in the Semantic Web Notations.
    J Biomed Inform. 2013 Feb;46(1):128-38. doi: 10.1016/j.jbi.2012.09.003. Epub 2012 Sep 28. PubMed PMID: 23026232; PubMed Central PMCID: PMC3563768. [PubMed]
  110. Thibault JC, Frey L.
    Biomedical Terminology Mapper for UML projects.
    AMIA Summits Transl Sci Proc. 2013 Mar 18;2013:257-61. eCollection 2013. PubMed PMID:
    24303278; PubMed Central PMCID: PMC3845744. [PubMed]
  111. Trivela D, Stoilos G, Chortaras A, Stamou G.
    Optimising Resolution-Based Rewriting Algorithms for DL Ontologies.
    Proceedings of the 26th International Workshop on Description Logics (DL 2013), Ulm, Germany, July 23-26, 2013 (in CEUR Workshop Proceedings. CEUR-WS.org, July 2013. [PDF Exit Disclaimer logo ]
  112. Vergara-Niedermayr C, Wang F, Pan T, Kurc T, Saltz J.
    Semantically Interoperable XML Data.
    Int. J. Semantic Computing. 2013 Sept;7(3). DOI: 10.1142/S1793351X13500037 [Online Exit Disclaimer logo ]
  113. Wang Z, Sagotsky J, Taylor T, Shironoshita P, Deisboeck TS.
    Accelerating cancer systems biology research through Semantic Web technology.
    Wiley Interdiscip Rev Syst Biol Med. 2013 Mar;5(2):135-51. doi: 10.1002/wsbm.1200. Epub 2012 Nov 27. PubMed PMID: 23188758; PubMed Central PMCID: PMC3558557. [PubMed]
     
    2012
  114. Aguirre JL, Eckert K, Euzenat J, Ferrara A, van Hage WR, Hollink L, Meilicke C, Nikolov A, Ritze D, Scharffe F, Shvaiko P, Šváb-Zamazal O, Trojahn C, Jiménez-Ruiz E, Cuenca Grau B, Zapilko B.
    Results of the Ontology Alignment Evaluation Initiative 2012.
    Proceedings of the Seventh International Workshop on Ontology Matching (OM-2012), collocated with the 11th International Semantic Web Conference (ISWC-2012), Nov 11, 2012, Boston, MA USA. [PDF Exit Disclaimer logo ]
  115. Aguirre JL, Eckert K, Euzenat J, Ferrara A, van Hage WR, Hollink L, Meilicke C, Nikolov A, Ritze D, Scharffe F, Shvaiko P, Šváb-Zamazal O, Trojahn C, Jiménez-Ruiz E, Cuenca Grau B, Zapilko B.
    Preliminary results of the Ontology Alignment Evaluation Initiative 2012.
    Seventh International Workshop on Ontology Matching (ISWC 2012), 2012, Boston, Massachusetts, U.S.A. Nov 11, 2012. [PDF Exit Disclaimer logo ]
  116. Beisswanger E, Hahn U.
    Towards valid and reusable reference alignments – ten basic quality checks for ontology alignments and their application to three different reference data sets.
    Journal of Biomedical Semantics 2012, 3(Suppl 1):S4. [PDF Exit Disclaimer logo ]
  117. Benik J, Palma G, Raschid L, Thor A, Vidal ME.
    Mining Patterns from Clinical Trial Annotated Datasets by Exploiting the NCI Thesaurus.
    Proc. 11th Intl. Semantic Web Conference (ISWC 2012) 2012, Boston, Massachusetts, U.S.A. Nov 11-15, 2012. [PDF Exit Disclaimer logo ]
  118. Bougie G.
    A Case Study of a New Era in Disease Classification: An Investigation of the Socio-technical Requirements for Inclusive Standardization Development.
    Thesis (M.Sc.) University of Victoria, Department of Computer Science, 2012. [UVic Exit Disclaimer logo ]
  119. Chen ES, Melton GB, Burdick TE, Rosenau PT, Sarkar IN.
    Characterizing the use and contents of free-text family history comments in the Electronic Health Record.
    AMIA Annu Symp Proc. 2012;2012:85-92. Epub 2012 Nov 3. PubMed PMID: 23304276; PubMed Central PMCID: PMC3540518. [PubMed]
  120. Chen Y, Gu H, Perl Y, Geller J.
    Overcoming an obstacle in expanding a UMLS semantic type extent.
    J Biomed Inform. 2012 Feb;45(1):61-70. Epub 2011 Sep 9. PubMed PMID: 21925287. [PubMed]
  121. Chua WW, Kim JJ.
    BOAT: Automatic alignment of biomedical ontologies using term informativeness and candidate selection.
    J Biomed Inform. 2012 Apr;45(2):337-49. doi: 10.1016/j.jbi.2011.11.010. Epub 2011 Dec 2. PubMed PMID: 22155335. [PubMed]
  122. Diallo G, Ba M.
    Effective method for large scale ontology matching.
    Proceedings of the 5th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2012), eds. Paschke A, Burger A, Romano P, Marshall MS, Splendiani A. Paris, France, November 28-30, 2012. [PDF Exit Disclaimer logo ]
  123. Dos Reis JC, Pruski C, Da Silveira M, Reynaud-Delaître C.
    Analyzing and Supporting the Mapping Maintenance Problem in Biomedical Knowledge Organization Systems.
    Semantic Interoperability in Medical Informatics (SIMI 2012). Heraklion (Crete), Greece, May 27, 2012. [PDF Exit Disclaimer logo ]
  124. Ferreira JD, Pesquita C, Couto FM, Silva MJ.
    Bringing epidemiology into the Semantic Web.
    Proc of the 3rd International Conference on Biomedical Ontology (ICBO 2012). 2012 July 21-25. Graz, Austria. [PDF Exit Disclaimer logo ]
  125. Freimuth RR, Freund ET, Schick L, Sharma MK, Stafford GA, Suzek BE, Hernandez J, Hipp J, Kelley JM, Rokicki K, Pan S, Buckler A, Stokes TH, Fernandez A, Fore I, Buetow KH, Klemm JD.
    Life sciences domain analysis model.
    J Am Med Inform Assoc. 2012 Nov-Dec;19(6):1095-102. doi: 10.1136/amiajnl-2011-000763. Epub 2012 Jun 28. PubMed PMID: 22744959; PubMed Central PMCID: PMC3486731. [PubMed]
  126. Fung KW, Bodenreider O.
    Knowledge Representation and Ontologies.
    In: Richesson RL, Andrews JE, editors. Clinical research informatics. New York: Springer; 2012. Chapter 14, pp.255-275. [Springer Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  127. Giudicelli V, Lefranc MP.
    IMGT-ONTOLOGY 2012.
    Front Genet. 2012;3:79. Epub 2012 May 23. PubMed PMID: 22654892; PubMed Central PMCID: PMC3358611. [PubMed]
  128. Gonçalves RS, Parsia B, Sattler U.
    Concept-Based Semantic Difference in Expressive Description Logics.
    Proceedings of the 25th International Workshop on Description Logics (DL 2012), Rome, Italy, June 7-10, 2012 (in CEUR Workshop Proceedings 846:191-201). [PDF Exit Disclaimer logo ]
  129. Gonçalves RS, Parsia B, Sattler U.
    Concept-Based Semantic Difference in Expressive Description Logics.
    11th International Semantic Web Conference (ISWC 2012) Boston, Massachusetts, U.S.A. Nov 11-15, 2012 (LNCS 7649:99-115).
  130. González-Beltrán A, Tagger B, Finkelstein A.
    Federated ontology-based queries over cancer data.
    BMC Bioinformatics 2012, 13(Suppl 1):S9. [BMC Exit Disclaimer logo ]
  131. Grabar N, Hamon T, Bodenreider O.
    Ontologies and terminologies: Continuum or dichotomy?
    Applied Ontology. 2012; 7:375–386. IOS Press, DOI 10.3233/AO-2012-0119.[PDF]
  132. Gross A, Hartung M, Kirsten T, Rahm E.
    GOMMA Results for OAEI 2012.
    Seventh International Workshop on Ontology Matching (ISWC 2012), 2012, Boston, Massachusetts, U.S.A. Nov 11, 2012. [PDF Exit Disclaimer logo ]
  133. Gross A, Hartung M, Thor A, Rahm E.
    How do computed ontology mappings evolve? - A case study for life science ontologies.
    Proceedings of the 2nd Joint Workshop on Knowledge Evolution and Ontology Dynamics (EvoDyn 2012), in conjuction with 11th Intl. Semantic Web Conference (ISWC 2012) 2012, Boston, Massachusetts, U.S.A. Nov 12, 2012. [PDF Exit Disclaimer logo ]
  134. Gross A, Hartung M, Thor A, Rahm E.
    How do Ontology Mappings Change in the Life Sciences?
    CoRR abs/1204.2731 April 2012. [PDF Exit Disclaimer logo ]
  135. Hartung M, Gross A, Kirsten T, Rahm E.
    Effective Composition of Mappings for Matching Biomedical Ontologies.
    9th Extended Semantic Web Conference (ESWC 2012), Heraklion, Crete, Greece, May 27-31, 2012. To appear in ESWC 2012 Workshops, Revised Selected Papers (LNCS) 2012-09. [Leipzig Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  136. Hartung M, Gross A, Kirsten T, Rahm E.
    Effective Mapping Composition for Biomedical Ontologies.
    Semantic Interoperability in Medical Informatics (SIMI 2012). Heraklion ,Crete, Greece, May 27, 2012. [PDF Exit Disclaimer logo ]
  137. Ivanova V, Lambrix P.
    A System for Debugging Taxonomies and their Alignments.
    First International Workshop on Debugging Ontologies and Ontology Mappings, Galway, Ireland, Oct 8 2012. [PDF Exit Disclaimer logo ]
  138. Jiang G, Solbrig HR, Chute CG.
    Quality evaluation of value sets from cancer study common data elements using the UMLS semantic groups.
    J Am Med Inform Assoc. 2012 Jun 1;19(e1):e129-e136. Epub 2012 Apr 17. PubMed PMID: 22511016; PubMed Central PMCID: PMC3392855. [PubMed]
  139. Jimenez-Ruiz E, Cuenca Grau B, Horrocks I.
    Exploiting the UMLS Metathesaurus in the Ontology Alignment Evaluation Initiative.
    Proceedings of the 2nd International Workshop on Exploiting Large Knowledge Repositories (E-LKR'12) Castellón de la Plana, Spain, September 7, 2012. [PDF Exit Disclaimer logo ]
  140. Konev B, Ludwig M, Walther D, Wolter F.
    The Logical Diff for the Lightweight Description Logic EL.
    Journal of Artificial Intelligence Research, 2012 (Submitted). [PDF Exit Disclaimer logo ]
  141. Konev B, Ludwig M, Wolter F.
    Logical Difference Computation with CEX2.5.
    Proceedings of the 6th International Joint Conference on Automated Reasoning (IJCAR 2012). Springer-Verlag, 2012. [PDF Exit Disclaimer logo ]
  142. Lacson R, Andriole KP, Prevedello LM, Khorasani R.
    Information from Searching Content with an Ontology-Utilizing Toolkit (iSCOUT).
    J Digit Imaging. 2012 Aug;25(4):512-9. PubMed PMID: 22349993; PubMed Central PMCID: PMC3389089. [PubMed] [Online Exit Disclaimer logo ]
  143. Lambrix P, Ivanova V, Dragisic Z.
    Contributions of LiU/ADIT to Debugging Ontologies and Ontology Mappings.
    In Lambrix P, ed. Advances in Secure and Networked Information Systems – The ADIT Perspective, Dec. 2012, pp.109-120. [PDF Exit Disclaimer logo ]
  144. Machado CM, Freitas AT, Couto FM.
    Enrichment analysis applied to disease prognosis.
    Proceedings of the 4th Workshop of the GI Workgroup Ontologies in Biomedicine and Life Sciences (OBML 2012) eds. Boeker M, Herre H, Hoehndorf R, Loebe F. Dresden, Germany, Sept 27-28, 2012. [PDF Exit Disclaimer logo ]
  145. Massot M, Cuggia M, Duvauferrier R, Bertaud-Gounot V.
    Représentation formelle des critères d’éligibilité aux essais cliniques: Exigences sémantiques de représentation des relations.
    In: Staccini P, Harmel A, Darmoni SJ, Gouider R, eds. Systèmes d’information pour l’amélioration de la qualité en santé. 2012:37-46. [Springer Exit Disclaimer logo ]
  146. Mathur S.
    Ontology-Based Methods for Disease Similarity Estimation and Drug Repositioning.
    Thesis (Ph.D.) University of Missouri Kansas City, Computer Science and Mathematics, 2012. [PDF Exit Disclaimer logo ]
  147. McCusker J, Lee J, Thomas C, McGuinness DL.
    Public Health Surveillance Using Global Health Explorer.
    Joint Workshop on Semantic Technologies Applied to Biomedical Informatics and Individualized Medicine (SATBI+SWIM 2012) in conjunction with International Semantic Web Conference (ISWC 2012). Boston, Massachusetts, U.S.A. Nov 11-15, 2012. [PDF Exit Disclaimer logo ]
  148. Meilicke C, Svab-Zamazal O, Trojahn C, Jimenez-Ruiz E, Aguirre JL, Stuckenschmidt H, Cuenca Grau B.
    Evaluating Ontology Matching Systems on Large, Multilingual and Real-world Test Cases.
    Technical Report of the OAEI 2011.5 Evaluation Campaign ArXiv e-prints, vol. 1208.3148, August, 2012. [PDF Exit Disclaimer logo ]
  149. Min H, Wojtusiak J.
    Clinical data analysis using ontology-guided rule learning.
    Proceedings of the 2nd international workshop on Managing interoperability and compleXity in health systems (MIXHS '12) pp.17-22 (at 21st ACM International Conference on Information and Knowledge Management (CIKM'12) Maui, HI, USA Oct 29 - Nov 2, 2012. [ACM Exit Disclaimer logo ]
  150. Musen MA, Noy NF, Shah NH, Whetzel PL, Chute CG, Story MA, Smith B; NCBO team.
    The National Center for Biomedical Ontology.
    J Am Med Inform Assoc. 2012 Mar-Apr;19(2):190-5. doi: 10.1136/amiajnl-2011-000523. Epub 2011 Nov 10. PubMed PMID: 22081220; PubMed Central PMCID: PMC3277625. [PubMed]
  151. Pathak J, Kiefer RC, Bielinski SJ, Chute CG.
    Applying semantic web technologies for phenome-wide scan using an electronic health record linked Biobank.
    J Biomed Semantics. 2012 Dec 17;3(1):10. doi: 10.1186/2041-1480-3-10. PubMed PMID: 23244446; PubMed Central PMCID: PMC3554594. [PubMed]
  152. Pathak J, Kiefer RC, Chute CG.
    Using Semantic Web Technologies for Cohort Identification from Electronic Health Records for Clinical Research.
    AMIA Clinical Research Informatics Summit 2012 Mar. [PDF Exit Disclaimer logo ]
  153. Pesquita CLSC.
    Automated Extension of Biomedical Ontologies.
    Thesis (Ph.D.) Universidade de Lisboa, Faculdade de Ciências, Departamento de Informática 2012. [U.Lisbon Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  154. Queralt-Rosinach N, Furlong LI.
    DisGeNET: from MySQL to Nanopublication, Modelling Gene-Disease Associations for the Semantic Web.
    Proceedings of the 5th International Workshop on Semantic Web Applications and Tools for Life Sciences (SWAT4LS 2012), eds. Paschke A, Burger A, Romano P, Marshall MS, Splendiani A. Paris, France, November 28-30, 2012. [PDF Exit Disclaimer logo ]
  155. Saitwal H, Qing D, Jones S, Bernstam E, Chute CG, Johnson TR.
    Cross-Terminology Mapping Challenges: A Demonstration Using Medication Terminological Systems.
    J Biomed Inform. 2012 Aug;45(4):613-25. Epub 2012 Jun 28. PubMed PMID: 22750536. [PubMed]
  156. Silwal P.
    Ontology Alignment Using Semantic Similarity with Refernce Ontologies.
    Thesis (M.Sc.) Miami University, Department of Computer Science and Software Engineering, Oxford, Ohio, 2012. [PDF Exit Disclaimer logo ]
  157. Szwabe A, Misiorek P, Walkowiak P.
    Reflective Relational Learning for Ontology Alignment.
    Distributed Computing and Artificial Intelligence, Omatu S, et al., eds. Springer-Verlag; 2012. pp. 519-526. [Springer Exit Disclaimer logo ]
  158. Tagaris A, Chondrogiannis E, Andronikou V, Tsatsaronis G, Mourtzoukos K, Roumier J, Matskanis N, Schroeder M, Massonet P, Koutsouris D, Varvarigou T.
    Semantic Interoperability between Clinical Research and Healthcare: the PONTE approach.
    Semantic Interoperability in Medical Informatics (SIMI 2012). Heraklion (Crete), Greece, May 27, 2012. [Abstract Exit Disclaimer logo ]
  159. Vergara-Niedermayr C, Wang F, Pan T, Kurc T, Saltz J.
    Semantically Interoperable XML Data.
    Emory University Center for Comprehensive Informatics Technical Report CCI-TR-2012-1, January 12, 2012. [Emory Exit Disclaimer logo ]
  160. Walk S.
    Pragmatic analysis of collaborative ontology engineering processes.
    Thesis (M.Sc.) Graz University of Technology, Knowledge Management Institute, Austria, April 19, 2012. [PDF Exit Disclaimer logo ]
  161. Wu ST, Liu H, Li D, Tao C, Musen MA, Chute CG, Shah NH.
    Unified Medical Language System term occurrences in clinical notes: a large-scale corpus analysis.
    J Am Med Inform Assoc. 2012 Jun;19(1e):e149-56. Epub 2012 Apr 4. PubMed PMID: 22493050; PubMed Central PMCID: PMC3392861. [PubMed]
  162. Zheng S, Wang F, Lu J, Saltz J.
    Enabling Ontology Based Semantic Queries in Biomedical Database Systems.
    Emory University Center for Comprehensive Informatics Technical Report CCI-TR-2012-3, March 20, 2012. [Emory Exit Disclaimer logo ]
     
    2011
  163. Bail S, Horridge M, Parsia B, Sattler U.
    The justificatory structure of the NCBO bioportal ontologies.
    The Semantic Web–ISWC 2011. Springer 2011 pp.67-82. [PDF Exit Disclaimer logo ]
  164. Bail S, Parsia B, Sattler U.
    Extracting finite sets of entailments from OWL ontologies.
    Proc. of DL-11. 2011. [PDF Exit Disclaimer logo ]
  165. Brochhausen M, Spear AD, Cocos C, Weiler G, Martín L, Anguita A, Stenzhorn H, Daskalaki E, Schera F, Schwarz U, Sfakianakis S, Kiefer S, Dörr M, Graf N, Tsiknakis M.
    The ACGT Master Ontology and its applications--towards an ontology-driven cancer research and management system.
    J Biomed Inform. 2011 Feb;44(1):8-25. Epub 2010 May 11. Review. PubMed PMID: 20438862. [PubMed]
  166. Dentler K, Cornet R, ten Teije A, de Keizer N.
    Comparison of reasoners for large ontologies in the OWL 2 EL profile.
    Semantic Web Journal, 2(2):71-87,2011. [Online Exit Disclaimer logo ]
  167. Falconer SM, Tudorache T, Noy NF.
    An Analysis of Collaborative Patterns in Large-Scale Ontology Development Projects.
    Proceedings of the sixth international conference on Knowledge capture (K-CAP '11). 2011 ACM, New York, NY, USA. [ACM Exit Disclaimer logo ] [Stanford Exit Disclaimer logo ]
  168. Gennari JH, Neal ML, Galdzicki M, Cook DL.
    Multiple ontologies in action: composite annotations for biosimulation models.
    J Biomed Inform. 2011 Feb;44(1):146-54. Epub 2010 Jun 30. PubMed PMID: 20601121; PubMed Central PMCID: PMC2989341. [PubMed]
  169. Ghazvinian A, Noy NF, Musen MA.
    From mappings to modules: Using mappings to identify domain-specific modules in large ontologies.
    KCAP 2011 - Proceedings of the 2011 Knowledge Capture Conference, 2011, pp. 33-40. [Scopus Exit Disclaimer logo ]
  170. Gonçalves RS, Parsia B, Sattler U.
    Categorising logical differences between OWL ontologies.
    20th ACM Conference on Information and Knowledge Management (CIKM'11) (2011) Glasgow, 2011 Oct. 24-28, pp.1541-1546. [At IEEE Xplore Exit Disclaimer logo ]
  171. Gradie PR, Litster M, Thomas R, Vyas J, Schiller MR.
    SciReader enables reading of medical content with instantaneous definitions.
    BMC Med Inform Decis Mak. 2011 Jan 25;11:4. PubMed PMID: 21266060; PubMed Central PMCID: PMC3038137. [PubMed]
  172. Gross A, Hartung M, Kirsten T, Rahm E.
    Mapping Composition for Matching Large Life Science Ontologies.
    Proc of the 2nd International Conference on Biomedical Ontology (ICBO) 2011. [PDF Exit Disclaimer logo ]
  173. Groza T, Zankl A, Li YF, Hunter J.
    Using Semantic Web Technologies to Build a Community-Driven Knowledge Curation Platform for the Skeletal Dysplasia Domain.
    Proceedings of the 10th International Semantic Web Conference (ISWC 2011), Springer, Bonn, Germany, October 23-27, 2011. [PDF Exit Disclaimer logo ]
  174. Jiang G, Solbrig HR, Chute CG.
    Quality evaluation of cancer study Common Data Elements using the UMLS Semantic Network.
    J Biomed Inform. 2011 Dec;44 Suppl 1:S78-85. Epub 2011 Aug 5. PubMed PMID: 21840422. [PubMed]
  175. Jiménez-Ruiz E, Grau BC, Horrocks I, Berlanga R.
    Logic-based assessment of the compatibility of UMLS ontology sources.
    J Biomed Semantics. 2011 Mar 7;2 Suppl 1:S2. PubMed PMID: 21388571; PubMed Central PMCID: PMC3105494. [PubMed]
  176. Jonquet C, LePendu P, Falconer S, Coulet A, Noy NF, Musen MA, Shah NH
    NCBO Resource Index: Ontology-based search and mining of biomedical resources
    Web Semantics: Science, Services and Agents on the World Wide Web, Volume 9, Issue 3, September 2011, Pages 316-324, ISSN 1570-8268, 10.1016/j.websem.2011.06.005. [PDF Exit Disclaimer logo ]
  177. Khoo CSG, Na JC, Wang VW, Chan S.
    Developing an Ontology for Encoding Disease Treatment Information in Medical Abstracts.
    DESIDOC Journal of Library and Information Technology, 2011 Mar;31(2):103-115. [DESIDOC Exit Disclaimer logo ] [PDF]
  178. Kirsten T, Gross A, Hartung M, Rahm E.
    GOMMA: a component-based infrastructure for managing and analyzing life science ontologies and their evolution.
    J Biomed Semantics. 2011 Sep 13;2:6. PubMed PMID: 21914205; PubMed Central PMCID: PMC3198872. [PubMed] [Free PMC]
  179. Lee S, Choi J, Park K, Song M, Lee D.
    Inferring hidden relationships from biological literature with multi-level context terms.
    Proceedings of the ACM fifth international workshop on Data and text mining in biomedical informatics (DTMBIO '11). 2011: ACM New York, NY, USA. [ACM Exit Disclaimer logo ]
  180. Lim Choi Keung SN, Zhao L, Ogunsina I, Arvanitis TN, Tyler E.
    A Framework for Vocabulary Controlled Queries of Distributed Electronic Healthcare Records.
    Proc 3rd International Workshop on Knowledge Representation for Health Care (KR4HC 2011) in conjunction with the 13th Conference on Artificial Intelligence in Medicine (AIME 2011), pp. 196-209, 2011. [PDF Exit Disclaimer logo ] [IOS Press Exit Disclaimer logo ]
  181. Liu K, Chapman WW, Savova G, Chute CG, Sioutos N, Crowley RS.
    Effectiveness of Lexico-syntactic Pattern Matching for Ontology Enrichment with Clinical Documents.
    Methods Inf Med. 2011;50(5):397-407. Epub 2010 Nov 8. [PubMed]
  182. Luciano JS, Andersson B, Batchelor C, Bodenreider O, Clark T, Denney CK, Domarew C, Gambet T, Harland L, Jentzsch A, Kashyap V, Kos P, Kozlovsky J, Lebo T, Marshall SM, McCusker JP, McGuinness DL, Ogbuji C, Pichler E, Powers RL, Prud'hommeaux E, Samwald M, Schriml L, Tonellato PJ, Whetzel PL, Zhao J, Stephens S, Dumontier M.
    The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside.
    J Biomed Semantics. 2011 May 17;2 Suppl 2:S1. PubMed PMID: 21624155; PubMed Central PMCID: PMC3102889. [PubMed] [Free PMC Article]
  183. Maier D, Kalus W, Wolff M, Kalko SG, Roca J, Marin de Mas I, Turan N, Cascante M, Falciani F, Hernandez M, Villà-Freixa J, Losko S.
    Knowledge management for systems biology a general and visually driven framework applied to translational medicine.
    BMC Syst Biol. 2011 Mar 5;5:38. PubMed PMID: 21375767; PubMed Central PMCID: PMC3060864. [PubMed]
  184. Martin M.
    Semantic Web may be cancer information's next step forward.
    J Natl Cancer Inst. 2011 Aug 17;103(16):1215-8. Epub 2011 Aug 3. PubMed PMID: 21813408. [PubMed Exit Disclaimer logo ] [Full Text Exit Disclaimer logo ]
  185. McCusker JP, Luciano JS, McGuinness DL.
    Towards an Ontology for Conceptual Modeling.
    Proc. of the International Conference on Biomedical Ontology, 2011. [Online Exit Disclaimer logo ]
  186. Payne PR, Borlawsky TB, Lele O, James S, Greaves AW.
    The TOKEn project: knowledge synthesis for in silico science.
    J Am Med Inform Assoc. 2011 Dec;18 Suppl 1:i125-31. doi: 10.1136/amiajnl-2011-000434. Epub 2011 Oct 7. PubMed PMID: 21984589; PubMed Central PMCID: PMC3241179. [PubMed]
  187. Shamdasani J, Bloodsworth P, Hauer T, Branson A, Rogulin D, Odeh M, McClatchey R.
    MedMatch: Applying Semantic Matching in the Medical Domain.
    Journal of Web Semantics 2011 (Submitted). [PDF Exit Disclaimer logo ]
  188. Shaw M, Detwiler LT, Noy N, Brinkley J, Suciu D.
    vSPARQL: a view definition language for the semantic web.
    J Biomed Inform. 2011 Feb;44(1):102-17. Epub 2010 Aug 25. PubMed PMID: 20800106; PubMed Central PMCID: PMC3042057. [PubMed]
  189. Tudorache T, Musen MA.
    Collaborative Development of Large-Scale Biomedical Ontologies.
    Collaborative Computational Technologies for Biomedical Research (eds S. Ekins, M. A. Z. Hupcey and A. J. Williams), John Wiley & Sons, Inc., Hoboken, NJ, USA; 2011. pp. 179-200. doi: 10.1002/9781118026038.ch12
     
    2010
  190. Amin W, Kang HP, Becich MJ.
    Data Management, Databases, and Warehousing.
    Biomedical Informatics for Cancer Research. (M.F. Ochs, J.T. Casagrande, R.V. Davuluri, eds). DOI 10.1007/978-1-4419-5714-6_3, © Springer Science+Business Media, LLC 2010. pp.39-71.
  191. Chua WWK, Goh AES.
    An asymmetric similarity measure for ontologies based on the feature contrast model.
    Proceedings of the 2010 International Conference on Complex, Intelligent and Software Intensive Systems (CISIS '10), Krakow, Poland, 16 February 2010. IEEE Computer Society Washington, DC, USA ©2010. pp.1002–1007.
  192. Duan W, Yates A.
    Extracting glosses to disambiguate word senses.
    Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, Stroudsburg, PA, 2010 pp.627-635. [PDF Exit Disclaimer logo ]
  193. González-Beltrán A, Tagger B, Finkelstein A.
    Ontology-based Queries over Cancer Data.
    Proceedings of the 3rd International Workshop on Semantic Web Applications and Tools for the Life Sciences, eds. Paschke A, Burger A, Splendiani A, Marshall MS, Romano P. Berlin, Germany, Dec 8-10, 2010. [PDF Exit Disclaimer logo ]
  194. Hartung M, Gross A, Kirsten T, Rahm E.
    Discovering evolving regions in life science ontologies.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 6254 LNBI, 2010, pp. 19-34. [Scopus Exit Disclaimer logo ]
  195. Jonquet C, Musen MA, Shah NH.
    Building a biomedical ontology recommender web service.
    J Biomed Semantics. 2010 Jun 22;1 Suppl 1:S1. PubMed PMID: 20626921; PubMed Central PMCID: PMC2903720. [PubMed] [PDF Exit Disclaimer logo ]
  196. Kang YB, Li YF, Krishnaswamy S.
    Predicting Reasoning Performance Using Ontology Metrics.
    The Journal of Systems and Software, 83 (2010):803–814. [PDF Exit Disclaimer logo ]
  197. Martinez-Romero M, Vazquez-Naya J, Munteanu CR, Pereira J, Pazos A.
    An approach for the automatic recommendation of ontologies using collaborative knowledge.
    Proceedings of the 14th international conference on Knowledge-based and intelligent information and engineering systems: Part II (KES\'10), Rossitza Setchi, Ivan Jordanov, Robert J. Howlett, and Lakhmi C. Jain (Eds.). Springer-Verlag, Berlin, Heidelberg, 74-81.
  198. Obeid J, Gabriel D, Sanderson I.
    A biomedical research permissions ontology: cognitive and knowledge representation considerations.
    Proceedings of the 2010 Workshop on Governance of Technology, Information and Policies (GTIP '10). ACM, New York, NY, USA, 9-13. DOI=10.1145/1920320.1920322 doi.acm,org Exit Disclaimer logo
  199. Parai GK, Jonquet C, Xu R, Musen MA, Shah NH.
    The Lexicon Builder Web service: Building Custom Lexicons from two hundred Biomedical Ontologies.
    AMIA Annu Symp Proc. 2010 Nov 13;2010:587-91. PubMed PMID: 21347046; PubMed Central PMCID: PMC3041331. [PubMed] [PDF]
  200. Payne PR, Borlawsky TB, Stephens W, Barrett MC, Nguyen-Pham T, Greaves AW.
    The TRITON Project: Design and Implementation of an Integrative Translational Research Information Management Platform.
    AMIA Annu Symp Proc. 2010 Nov 13;2010:617-21. PubMed PMID: 21347052; PubMed Central PMCID: PMC3041280. [PubMed] [Full Text]
  201. Schulz S, Schober D, Tudose I, Stenzhorn H.
    The Pitfalls of Thesaurus Ontologization - the Case of the NCI Thesaurus.
    AMIA Annu Symp Proc. 2010 Nov 13;2010:727-31. PubMed PMID: 21347074; PubMed Central PMCID: PMC3041372. [PubMed]
  202. Sun P, Zhang S.
    Identifying Granularity Differences between Large Biomedical Ontologies through Rules.
    AMIA Annu Symp Proc. 2010 Nov 13;2010:927-31. PubMed PMID: 21347114; PubMed Central PMCID: PMC3041335. [PubMed] [Full Text]
  203. Zhang H, Li YF, Tan HBK.
    Measuring Design Complexity of Semantic Web Ontologies.
    Journal of Systems and Software, 83(5):803-814, 2010. [PDF Exit Disclaimer logo ] [HTML Exit Disclaimer logo ]
     
    2009
  204. Borlawsky TB, Dhaval R, Hastings SL, Payne PR.
    Development of an agile knowledge engineering framework in support of multi-disciplinary translational research.
    Summit on Translat Bioinforma. 2009 Mar 1;2009:14-8. PubMed PMID: 21347164; PubMed Central PMCID: PMC3041563. [PubMed]
  205. Ghazvinian A, Noy NF, Musen MA.
    Creating mappings for ontologies in biomedicine: simple methods work.
    AMIA Annu Symp Proc. 2009 Nov 14;2009:198-202. PubMed PMID: 20351849; PubMed Central PMCID: PMC2815474. [PubMed]
  206. Jonquet C, Shah NH, Musen MA.
    Prototyping a Biomedical Ontology Recommender Service.
    Bio-Ontologies: Knowledge in Biology, Stockholm, Sweden (2009).
  207. Jonquet C, Shah NH, Musen MA.
    The open biomedical annotator.
    Summit on Translat Bioinforma. 2009 Mar 1;2009:56-60. PubMed PMID: 21347171; PubMed Central PMCID: PMC3041576. [PubMed] [Full Text]
  208. Kementsietsidis A, Lim L, Wang M.
    Profile-based retrieval of records in medical databases.
    AMIA Annu Symp Proc. 2009 Nov 14;2009:312-6. PubMed PMID: 20351871; PubMed Central PMCID: PMC2815445. [PubMed]
  209. McCusker JP, Phillips JA, González Beltrán A, Finkelstein A, Krauthammer M.
    Semantic web data warehousing for caGrid.
    BMC Bioinformatics. 2009 Oct 1;10 Suppl 10:S2. PubMed PMID: 19796399; PubMed Central PMCID: PMC2755823. [PubMed]
  210. Motik B, Shearer R, Horrocks I.
    Hypertableau Reasoning for Description Logics.
    Journal of Artificial Intelligence Research, 2009 Oct;36:165-228. [PDF Exit Disclaimer logo ]
  211. Shah NH, Bhatia N, Jonquet C, Rubin D, Chiang AP, Musen MA.
    Comparison of concept recognizers for building the Open Biomedical Annotator.
    BMC Bioinformatics. 2009 Sep 17;10 Suppl 9:S14. PubMed PMID: 19761568; PubMed Central PMCID: PMC2745685. [PubMed] [Full Text Exit Disclaimer logo ]

    2008
  212. Allemang D, Hendler J.
    Semantic Web for the Working Ontologist: Effective Modeling in RDFS and OWL.
    Elsevier, 2008.
  213. Bodenreider O.
    Biomedical ontologies in action: role in knowledge management, data integration and decision support.
    Yearbook of Medical Informatics (2008) pp.67-79.
  214. Bodenreider O.
    Comparing SNOMED CT and the NCI Thesaurus through Semantic Web technologies.
    Proceedings of the Third International Conference on Knowledge Representation in Medicine (KR-MED 2008), 2008: p. 37-43. [PDF Exit Disclaimer logo ]
  215. Cohen B, Oren M, Min H, Perl Y, Halper M.
    Automated comparative auditing of NCIT genomic roles using NCBI.
    J Biomed Inform. 2008 Dec;41(6):904-13. Epub 2008 Mar 28. PubMed PMID: 18486558; PubMed Central PMCID: PMC2630966. [PubMed]
  216. Detwiler LT, Suciu D, Brinkley JF.
    Regular paths in SparQL: querying the NCI Thesaurus.
    AMIA Annu Symp Proc. 2008 Nov 6:161-5. PubMed PMID: 18999137; PubMed Central PMCID: PMC2656016. [PubMed]
  217. Jiménez-Ruiz E, Grau BC, Sattler U, Schneider T, Berlanga R.
    Safe and Economic Re-Use of Ontologies: A Logic-Based Methodology and Tool Support.
    Proceedings of the 5th European semantic web conference on the semantic web: research and applications. Berlin, Heidelberg; 2008. LNCS v.5021 p. 185–99. [Springer Exit Disclaimer logo ]
  218. Kementsietsidis A, Lim L, Wang M.
    Supporting ontology-based keyword search over medical databases.
    AMIA Annu Symp Proc. 2008 Nov 6:409-13. PubMed PMID: 18998839; PubMed Central PMCID: PMC2656038. [PubMed]
  219. Malone J, Rayner TF, Bradley XZ, Parkinson H.
    Developing an application focused experimental factor ontology: embracing the OBO Community.
    Bio-Ontologies SIG, ISMB 2008 Toronto Canada, 20 July 2008. [PDF Exit Disclaimer logo ]
  220. Min H, Cohen B, Halper M, Oren M, Perl Y.
    Detecting role errors in the gene hierarchy of the NCI Thesaurus.
    Cancer Inform. 2008;6:293-313. PubMed PMID: 19221606; PubMed Central PMCID: PMC2623310. [PubMed]
  221. Mougin F, Bodenreider O.
    Auditing the NCI thesaurus with semantic web technologies.
    AMIA Annu Symp Proc. 2008 Nov 6:500-4. PubMed PMID: 18999265; PubMed Central PMCID: PMC2655981. [PubMed]
  222. Noy N, Tudorache T, de Coronado S, Musen M.
    Developing biomedical ontologies collaboratively.
    AMIA Annu Symp Proc. 2008:520-4. [PubMed] [Available online]
  223. Payne PR, Borlawsky TB, Kwok A, Dhaval R, Greaves AW.
    Ontology-anchored Approaches to Conceptual Knowledge Discovery in a Multi-dimensional Research Data Repository.
    Summit on Translat Bioinforma. 2008 Mar 1;2008:85-9. PubMed PMID: 21347129; PubMed Central PMCID: PMC3041525. [PubMed]
  224. Redmond T, Smith M, Drummond N, Tudorache T.
    Managing Change: An Ontology Version Control System.
    OWL: Experiences and Directions, Procs of Fifth OWLED Workshop (2008), Karlsruhe, Germany, Oct 26-27, 2008. CEUR-WS.org (2008) [PDF Exit Disclaimer logo ]
  225. Rubin DL, Shah NH, Noy NF.
    Biomedical ontologies: a functional perspective.
    Brief Bioinform. 2008 Jan;9(1):75-90. Epub 2007 Dec 12. Review. PubMed PMID: 18077472. [PubMed]
  226. Sebastian A, Noy NF, Tudorache T, Musen MA.
    A generic ontology for collaborative ontology-development workflows.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 5268 LNAI, 2008, pp. 318-328. [Scopus Exit Disclaimer logo ]
  227. Shah NH, Musen MA.
    UMLS-Query: a perl module for querying the UMLS.
    AMIA Annu Symp Proc. 2008 Nov 6:652-6. PubMed PMID: 18998805; PubMed Central PMCID: PMC2656020. [PubMed] [Full Text]
     
    2007
  228. Dmitrieva J, Bei Y, Verbeek FJ.
    Ontological context visualization.
    OWLED 2007 Workshop on OWL. eds Golbreich C, Kalyanpur A, Parsia B. CEUR-WS.org (2007). [PDF Exit Disclaimer logo ]
  229. Zhang S, Bodenreider O.
    Experience in Aligning Anatomical Ontologies.
    Int J Semant Web Inf Syst. 2007;3(2):1-26. PubMed PMID: 18974854; PubMed Central PMCID: PMC2575410. [PubMed]
     
    2006
  230. Bernhard D.
    Automatic acquisition of semantic relationships from morphological relatedness.
    Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 4139 LNAI, 2006, pp. 121-132. [Scopus Exit Disclaimer logo ]
  231. Cimino JJ, Zhu X.
    The practical impact of ontologies on biomedical informatics.
    Yearb Med Inform. 2006:124-35. Review. PubMed PMID: 17051306. [PubMed]
  232. Cuenca Grau B, Parsia B, Sirin E, Kalyanpur A.
    Modularity and web ontologies.
    Proc. of KR-06, 2006. [PDF Exit Disclaimer logo ]
  233. Min H, Perl Y, Chen Y, Halper M, Geller J, Wang Y.
    Auditing as part of the terminology design life cycle.
    J Am Med Inform Assoc. 2006 Nov-Dec;13(6):676-90. Epub 2006 Aug 23. PubMed PMID: 16929044; PubMed Central PMCID: PMC1656963. [PubMed]
  234. Nadkarni PM, Brandt CA.
    The Common Data Elements for cancer research: remarks on functions and structure.
    Methods Inf Med. 2006;45(6):594-601. PubMed PMID: 17149500; PubMed Central PMCID: PMC2980785. [PubMed]
  235. Noy NF, Chugh A, Liu W, Musen MA.
    A Framework for Ontology Evolution in Collaborative Environments.
    The Semantic Web - ISWC 2006. Lecture Notes in Computer Science 4273 pp.544-558, DOI: 10.1007/11926078_39 [Springer Exit Disclaimer logo ] [PDF Exit Disclaimer logo ]
  236. Plikus MV, Zhang Z, Chuong CM.
    PubFocus: semantic MEDLINE/PubMed citations analytics through integration of controlled biomedical dictionaries and ranking algorithm.
    BMC Bioinformatics. 2006 Oct 2;7:424. PubMed PMID: 17014720; PubMed Central PMCID: PMC1618408. [PubMed] [Full Text]
     
    2005
  237. Crowley RS, Tseytlin E, Jukic D.
    ReportTutor - an intelligent tutoring system that uses a natural language interface.
    AMIA Annu Symp Proc. 2005:171-5. PubMed PMID: 16779024; PubMed Central PMCID: PMC1560511. [PubMed]
  238. Ceusters W, Smith B, Goldberg L.
    A terminological and ontological analysis of the NCI Thesaurus.
    Methods Inf Med. 2005;44(4):498-507. PubMed PMID: 16342916. [PubMed]
  239. Kumar A, Smith B.
    Oncology ontology in the NCI thesaurus.
    10th Conference on Artificial Intelligence in Medicine, AIME Aberdeen July 23-27 2005. In: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) LNAI:3581:213-220. [Scopus Exit Disclaimer logo ]
  240. Lintern R, Storey M.
    Jambalaya Express: Ontology Visualization-On-Demand. In
    8th Protege Conference, Madrid, Spain, July 18-21, 2005. [PDF Exit Disclaimer logo ]
  241. Supekar K, Chute CG, Solbrig H.
    Representing lexical components of medical terminologies in OWL.
    AMIA Annu Symp Proc. 2005:719-23. PubMed PMID: 16779134; PubMed Central PMCID: PMC1560674. [PubMed]

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