Discovering potential driver genes through an integrated model of somatic mutation profiles and gene functional information†

Abstract

The accumulating availability of next-generation sequencing data offers an opportunity to pinpoint driver genes that are causally implicated in oncogenesis through computational models. Despite previous efforts made regarding this challenging problem, there is still room for improvement in the driver gene identification accuracy. In this paper, we propose a novel integrated approach called IntDriver for prioritizing driver genes. Based on a matrix factorization framework, IntDriver can effectively incorporate functional information from both the interaction network and Gene Ontology similarity, and detect driver genes mutated in different sets of patients at the same time. When evaluated through known benchmarking driver genes, the top ranked genes of our result show highly significant enrichment for the known genes. Meanwhile, IntDriver also detects some known driver genes that are not found by the other competing approaches. When measured by precision, recall and F1 score, the performances of our approach are comparable or increased in comparison to the competing approaches.