This talk will discuss the role, effect, and quality of informatics in publicly funded research, from selection of proposals for funding to execution and sustainment. The case will be made that current funding mechanisms are mismatched to the scope, needs, and impact of informatics research by comparing and contrasting informatics projects with traditional experimental R&D proposals. Causes for poor quality informatics projects will be enumerated and suggestions given to address them.
Cancer patients and their doctors choose from a range of different treatment options. But often the chosen treatment is ineffective, reducing quality and length of life and increasing cost. Today treatment decisions and outcomes occur in isolation. All4Cure has built a patient-centered, web-based, knowledge sharing platform that graphically portrays treatments and responses extracted from the medical records of de-identified patients with multiple myeloma (the second most common form of blood cancer) for comment by a community of participating patients, clinicians and researchers. Having assembled more than 580 participants we will describe examples of patients have benefited from their participation.
Cancer is a complex category of diseases caused in large part by genetic or genomic, transcriptomic, proteomic, and epigenomics alterations leading to abnormal cell proliferation. Genes and their protein products rarely act in isolation. Therefore, it is necessary to utilize a comprehensive and integrated computational approach informed by systems biology and omics-oriented approaches to investigate the disruption of biological networks caused by genomic alterations.
In this talk, Dr. Meerzaman will describe two ongoing projects. The first focuses on Sequencing Quality Control Phase 2 (SEQC II), a collaborative project led by the Food and Drug Administration (FDA) that systematically investigated somatic mutations in paired breast cancer and normal cell lines and formulated best practices for identifying, or calling, genomic variations such as single-nucleotide polymorphisms, copy-number alterations, or single-nucleotide variants. Regarding the second project, Dr. Meerzaman will discuss methods developed by the CGBG team to use mutual exclusivity and pathway network interaction algorithms to identify low-frequency “driver” (that is, causative) genomic alterations at the pathway level.
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