Cancer genomes contain large numbers of somatic alterations but few genes drive tumor development. Identifying cancer driver genes is critical for precision oncology. Most of current approaches either identify driver genes based on mutational recurrence or using estimated scores predicting the functional consequences of mutations. 'driveR' is a tool for personalized or batch analysis of genomic data for driver gene prioritization by combining genomic information and prior biological knowledge. As features, 'driveR' uses coding impact metaprediction scores, non-coding impact scores, somatic copy number alteration scores, hotspot gene/double-hit gene condition, 'phenolyzer' gene scores and memberships to cancer-related KEGG pathways. It uses these features to estimate cancer-type-specific probability for each gene of being a cancer driver using the related task of a multi-task learning classification model. The method is described in detail in Ulgen E, Sezerman OU. 2020. driveR: A Novel Method for Prioritizing Cancer Driver Genes Using Somatic Genomics Data. bioRxiv <doi:10.1101/2020.11.10.376707>.
|Author||Ege Ulgen [aut, cre, cph] (<https://orcid.org/0000-0003-2090-3621>)|
|Maintainer||Ege Ulgen <firstname.lastname@example.org>|
|License||MIT + file LICENSE|
|Package repository||View on CRAN|
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