AI-Driver: an ensemble method for identifying driver mutations in personal cancer genomes.

ABSTRACT: The current challenge in cancer research is to increase the resolution of driver prediction from gene-level to mutation-level, which is more closely aligned with the goal of precision cancer medicine. Improved methods to distinguish drivers from passengers are urgently needed to dig out driver mutations from increasing exome sequencing studies. Here, we developed an ensemble method, AI-Driver (AI-based driver classifier, https://github.com/hatchetProject/AI-Driver), to predict the driver status of somatic missense mutations based on 23 pathogenicity features. AI-Driver has the best overall performance compared with any individual tool and two cancer-specific driver predicting methods. We demonstrate the superior and stable performance of our model using four independent benchmarks. We prov

SUBMITTER: Wang H 

PROVIDER: S-EPMC7671397 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature