Multi-Omic Data Improve Prediction of Personalized Tumor Suppressors and Oncogenes.

ABSTRACT: The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumor progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalized driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumor suppressor gene (TSG), oncogene (OG), or neutral, thus assigning the functional impact of the gene in the p

SUBMITTER: Sudhakar M 

PROVIDER: S-EPMC9127508 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature