EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.
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ABSTRACT:
SUBMITTER: Parvandeh S
PROVIDER: S-EPMC9262594 | biostudies-literature | 2022 Jul
REPOSITORIES: biostudies-literature
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EPIMUTESTR: a nearest neighbor machine learning approach to predict cancer driver genes from the evolutionary action of coding variants.
Parvandeh Saeid S Donehower Lawrence A LA Panagiotis Katsonis K Hsu Teng-Kuei TK Asmussen Jennifer K JK Lee Kwanghyuk K Lichtarge Olivier O
Nucleic acids research 20220701 12
Discovering rare cancer driver genes is difficult because their mutational frequency is too low for statistical detection by computational methods. EPIMUTESTR is an integrative nearest-neighbor machine learning algorithm that identifies such marginal genes by modeling the fitness of their mutations with the phylogenetic Evolutionary Action (EA) score. Over cohorts of sequenced patients from The Cancer Genome Atlas representing 33 tumor types, EPIMUTESTR detected 214 previously inferred cancer dr ...[more]
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