Effects of Multi-Omics Characteristics on Identification of Driver Genes Using Machine Learning Algorithms.

ABSTRACT: Cancer is a complex disease caused by genomic and epigenetic alterations; hence, identifying meaningful cancer drivers is an important and challenging task. Most studies have detected cancer drivers with mutated traits, while few studies consider multiple omics characteristics as important factors. In this study, we present a framework to analyze the effects of multi-omics characteristics on the identification of driver genes. We utilize four machine learning algorithms within this framework to detect cancer driver genes in pan-cancer data, including 75 characteristics among 19,636 genes. The 75 features are divided into four types and analyzed using Kullback-Leibler divergence based on CGC genes and non-CGC genes. We detect cancer driver genes in two different ways. One is to detect drive

SUBMITTER: Li F 

PROVIDER: S-EPMC9141966 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature