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EVS has enjoyed wide use and many collaborations within caBIG® and other organizations in the cancer research and biomedical community. Institutional profiles are followed by thematic efforts that span multiple institutions:

Institutional

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.

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.
    Emory University Center for Comprehensive Informatics Technical Report CCI-TR-2012-1, January 12, 2012. [Emory]
  2. 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]

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 [EVS: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 (below), 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 WHO section).

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

Mayo Clinic is the primary site for PHONT [http://informatics.mayo.edu/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 [[http:/sharpn.org/]] 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. 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.

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 [http://www.bioontology.org/] 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 [http://tma.stanford.edu] 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]
  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] [BMIR]
  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]
  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]
  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]
  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. 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]
  9. 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]
  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]
  11. 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]
  12. 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]
  13. 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]
  14. 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]
  15. 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]
  16. 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]

University of Pittsburgh

University of Pittsburgh uses the NCI Thesaurus (NCIt) cancer, anatomy, and pathology findings terminologies for their research and informatics projects. Several existing systems utilize NCIt and collaboration with NCIt has resulted in publication of several papers.

Ontology Development Information Extraction (ODIE)

ODIE [http://www.bioontology.org/ODIE] 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.

Cancer Text Information Extraction System (caTIES)

caTIES [http://caties.cabig.upmc.edu/] is an end-to-end Natural Language Processing retrieval system that encodes pathology reports to support access to human tissues. caTIES uses NCIm as the source of concepts for named entity recognition. It also relies on NCIt for ontology-based inference, indexing and retrieval. caTIES 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]
  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. 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]
  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. 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

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 [http://www.gwas.net] 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 [https://victr.vanderbilt.edu/eleMAP/] 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]

Human Studies Database (HSDB)

HSDB 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 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]

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 caBIG® and 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

caBIG® has established a Nanotechnology Working Group as part of its Integrative Cancer Research Workspace (ICR Nano WG), with a mission to federate nanotechnology databases. The Nanotechnology Working Group includes about 60 people representing approximately 40 agencies, universities and institutes, including some from Europe. One requirement of this group has been to develop data and vocabulary standards to facilitate federation and increase data accessibility. EVS is contributing to this effort as 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. 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, preparing NPO for integration into the NCI Metathesaurus (NCIm), and planning for future editing of NPO in the BiomedGT Wiki collaborative editing environment.

caNanoLab

The NCI CCNE office partnered with CBIIT 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. Funding for additional development was recently lost, but caNanoLab continues to be heavily used. It currently contains 886 samples, 72 sample sources, 3,329 characterizations, and 41 protocols, and identifies 1,073 publications. The caNanoLab homepage shows 141,668 visitors since June 3, 2010.

NanoTAB

Developed by CBIIT, Oregon State University, PNNL, Washington University St. Louis, Stanford, Jackson Labs, Pennsylvania BioNano Systems, NIOSH, NCI Frederick NCL, Emory/Georgia Tech. This tool is used by all members of the ICR Nano Working Group. Despite recent loss of caNanoLab funding, NanoTAB continues to be used heavily.

NanoParticle Ontology (NPO)

This terminology 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. 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]
  3. 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]

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 caBIG® use. (For more information, visit the OBO Foundry website.)

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. [Available online]

PhenX

PhenX [https://www.phenx.org/] 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 project for European health care reform [http://transformproject.eu/] is 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. TRANSFoRm has chosen to use LexEVS as its terminology server, but a new loader is required in support of the ATC terminology. The VKC is working with TRANSFoRm to develop a ATC loader, including requirements gathering, coding, testing, and documentation.

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