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
Ph.D. student Yun-Ching Chen is defending his dissertation Methods for genome interpretation: causal gene discovery and personal phenotype prediction. July 30, 2014 at 1pm. Clark 210 JHU Homewood Campus.
Dr. Karchin is presenting a talk on new algorithms to predict phenotypes from genomic information at Gordon Research Conference on Human Single Nucleotide Polymorphisms & Disease at Stonehill College in Easton, MA. Details
Dr. Masica is presenting a study of computational tools to determine the prognostic significance of TP53 mutation in head and neck squamous cell carcinoma (HNSCC) at the 2014 American Society of Clinical Oncology (ASCO) meeting in Chicago. Details
Dr. Karchin is co-chairing a Special Session on Post-genomic medical decision making in cancer at the 2014 Intelligent Systems for Molecular Biology (ISMB) meeting in Boston. ISMB Sessions Session Program