Identifying cancer driver mutations and genes from cancer big data

Currently, how to effectively identify driver mutations and genes in cancer genomes, especially those with the potential for druggable targets for the development of molecularly targeted cancer therapies, remains a major challenge. In this talk, I will first review the computational methods and tools that were recently developed to meet the strong demand on mining cancer big data. Then, I will present some informatics approaches for identifying cancer mutations and genes from large amount of somatic mutation data. In the last part, I will introduce our integrative network-based framework for identifying new druggable targets and anticancer indications from existing drugs.