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Bayesian Inference Of Cancer Driver Genes Using Signatures Of Positive Selection
[article]
2017
bioRxiv
pre-print
Tumors are composed of an evolving population of cells subjected to tissue-specific selection, which fuels tumor heterogeneity and ultimately complicates cancer driver gene identification. Here, we integrate cellular prevalence, population recurrence, and functional impact of somatic mutations as signatures of positive selection into a Bayesian model for driver prediction. We demonstrate that our model, cDriver, outperforms competing methods when analyzing solid tumors, hematological
doi:10.1101/059360
fatcat:cgpuh7hibzgjfjv6oldmmq3vra