Computational analysis of genomic and clinical data to aid medical decision making.
In the new "post-genome" era of personalized medicine, many variants critical to disease susceptibilities, prognosis and drug sensitivities will be identified and increased numbers of people will undergo DNA sequencing. We are developing algorithms and tools intended to facilitate this process.
We develop computational models to interpret and predict the impact of individual variation in the genome, transcriptome, and proteome. The models are being applied to cancer genomics, unclassified variants in Mendelian disease genes, and complex disease genetics. In collaboration with clinicians, pathologists, and experimental biologists, we aim to make significant improvements in individualized medicine within the next five years.
Mascia DL, Karchin R (2016) PLoS Computational Biology. 12(5):e1004725. Article
Niknafs N, Guthrie VB, Naiman DQ, Karchin R (2015) PLoS Computational Biology 11(10):e1004416 Article
Tokheim C Bhattacharya R, Niknafs N, Gygax DM, Kim R, Ryan M, Masica DL, Karchin R. May 2016 Article
David Masica presents webinar for Journal of American Medical Informatics Journal Club about "A novel approach for selecting combination clinical markers of pathology applied to a large retrospective cohort of surgically resected pancreatic cysts", Masica et al. JAMIA 2016. Thursday, August 11th, 2016, 3-4 ET. https://knowledge.amia.org/