Unsupervised detection of cancer driver mutations with parsimony-guided learning

Abstract

Methods are needed to reliably prioritize biologically active driver mutations over inactive passengers in high-throughput sequencing cancer data sets. We present ParsSNP, an unsupervised functional impact predictor that is guided by parsimony. ParsSNP uses an expectation–maximization framework to find mutations that explain tumor incidence broadly, without… (More)
DOI: 10.1038/ng.3658

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