MaxMIF: A New Method for Identifying Cancer Driver Genes through Effective Data Integration.

ABSTRACT: Identification of a few cancer driver mutation genes from a much larger number of passenger mutation genes in cancer samples remains a highly challenging task. Here, a novel method for distinguishing the driver genes from the passenger genes by effective integration of somatic mutation data and molecular interaction data using a maximal mutational impact function (MaxMIF) is presented. When evaluated on six somatic mutation datasets of Pan-Cancer and 19 datasets of different cancer types from TCGA, MaxMIF almost always significantly outperforms all the existing state-of-the-art methods in terms of predictive accuracy, sensitivity, and specificity. It recovers about 30% more known cancer genes in 500 top-ranked candidate genes than the best among the other tools evaluated. MaxMIF is also hi

SUBMITTER: Hou Y 

PROVIDER: S-EPMC6145398 | biostudies-literature | 2018 Sep

REPOSITORIES: biostudies-literature