Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2020.11.10.376707v1?rss=1 Authors: Ulgen, E., Sezerman, U. Abstract: Cancer develops due to "driver" genomic alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomic data. However, methods for personalized analysis of cancer driver genes are underdeveloped. In this study, we developed a novel personalized and batch analysis approach for driver gene prediction utilizing somatic genomic data, called driveR. Combining somatic genomic 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 our approach performs adequately, outperforms existing approaches, and is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes. Copy rights belong to original authors. Visit the link for more info