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driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics Data
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Abstract
Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomic 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 genomic data, called driveR. Combining 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 driveR performs adequately, outperforms existing approaches, and is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes. driveR is available on CRAN:
https://cran.r-project.org/package=driveR
.
Title: driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics Data
Description:
Abstract
Cancer develops due to “driver” alterations.
Numerous approaches exist for predicting cancer drivers from cohort-scale genomic 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 genomic data, called driveR.
Combining 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 driveR performs adequately, outperforms existing approaches, and is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes.
driveR is available on CRAN:
https://cran.
r-project.
org/package=driveR
.
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