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.
Collections of simultaneously altered genes as biomarkers of cancer cell drug response Cancer Research. 73(6):1699-70. Article
A hybrid likelihood model for sequence-based disease association studies PLoS Genetics. 9(1): e1003224 Article
Correlation among somatic mutation expression identifies genes important in human glioblastoma progression and survival. Cancer Research. Jul 1;71(13):4550-61 Article
A probabilistic model to predict clinical phenotypic traits from genome sequencing. PLoS Computational Biology. Sep 4. 10(9):e1003825 Article
Visualization of Ebola mutations. Mutation Position Imaging Toolbox for Ebola virus proteins released, in collaboration with the UCSC Ebola Portal Nov 15, 2014.
Dr. Karchin will teach Foundations of Computational Biology and Bioinformatics II, BME 580.688 during Spring semester 2015.
Congratulations to lab member Dr. Yun-Ching Chen, who received his Ph.D. in Biomedical Engineering! Sept 30, 2014. Dr. Chen is now a Postdoctoral Fellow at the NIH National Human Genome Research Institute.