DriveR: a novel method for prioritizing cancer driver genes using somatic genomics data.

ABSTRACT:

Background

Cancer develops due to "driver" alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR.

Results

Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651-0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0-1) on the 5157 personalized analysis

SUBMITTER: Ulgen E 

PROVIDER: S-EPMC8142487 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature