Genome-wide investigation of gene-cancer associations for the prediction of novel therapeutic targets in oncology.

ABSTRACT: A major cause of failed drug discovery programs is suboptimal target selection, resulting in the development of drug candidates that are potent inhibitors, but ineffective at treating the disease. In the genomics era, the availability of large biomedical datasets with genome-wide readouts has the potential to transform target selection and validation. In this study we investigate how computational intelligence methods can be applied to predict novel therapeutic targets in oncology. We compared different machine learning classifiers applied to the task of drug target classification for nine different human cancer types. For each cancer type, a set of "known" target genes was obtained and equally-sized sets of "non-targets" were sampled multiple times from the human protein-coding genes. Mod

SUBMITTER: Bazaga A 

PROVIDER: S-EPMC7330039 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature