Computational analysis of genomic data to aid medical decision making.
In the new "post-genome" era of personalized medicine, many variants critical to disease susceptibilities and drug sensitivies will be identified and increased numbers of people will undergo genetic testing. 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.
Niknafs N, Guthrie VB, Naiman DQ, Karchin R (2015) PLoS Computational Biology 11(10):e1004416 Article
Masica DL, Sosnay PR, Raraigh KS, Cutting GR, Karchin R (2015) Human Mol. Genetics. 24(7):1908-17 Article
Springer S, Wang Y, Dal Molin M, Masica DL et al. Article
A probabilistic model to predict clinical phenotypic traits from genome sequencing by Chen et al. named one of the best Translational Bioinformatics papers of the past year by the AMIA.
New MuPIT interactive viewer released Follow @CravatMupitTool
Asst. Prof. David Masica receives prize for top oral presentation in session on Diagnosis and Treatment of Pancreatic Tumors at United European Gastroenterology meeting in Barcelona, Spain Oct. 2015
Inter-discplinary pancreatic cyst classification project featured in Hopkins In Health Article