Major public and private efforts are underway to systematically resequence tumor genomes, with the hope that analysis of this data will fundamentally change the way cancer is treated. By identifying the oncogenic addictions and altered pathways in a certain cancer type or subtype, the cancer genomics community aims to help pinpoint targeted therapies or combinations of therapies that will most benefit individual patients, and to shorten time and reduce costs involved in drug development. I will discuss computational methods being developed in my group to assist these efforts. CHASM is a machine learning method that attempts to find genes not previously linked to tumorigenesis that may be somatically mutated in a relatively small fraction of tumors, but are important for tumor initiation or progression. MVOCA is a model-free approach to find pairs of genes whose alterations (e.g. differential expression, mutation) are correlated in a cancer genomics study.
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