Identification of Drug-Induced Liver Injury Biomarkers from Multiple Microarrays Based on Machine Learning and Bioinformatics Analysis.

ABSTRACT: Drug-induced liver injury (DILI) is the most common adverse effect of numerous drugs and a leading cause of drug withdrawal from the market. In recent years, the incidence of DILI has increased. However, diagnosing DILI remains challenging because of the lack of specific biomarkers. Hence, we used machine learning (ML) to mine multiple microarrays and identify useful genes that could contribute to diagnosing DILI. In this prospective study, we screened six eligible microarrays from the Gene Expression Omnibus (GEO) database. First, 21 differentially expressed genes (DEGs) were identified in the training set. Subsequently, a functional enrichment analysis of the DEGs was performed. We then used six ML algorithms to identify potentially useful genes. Based on receiver operating characteristi

SUBMITTER: Wang K 

PROVIDER: S-EPMC9570393 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature