Identification of early liver toxicity gene biomarkers using comparative supervised machine learning.

ABSTRACT: Screening agrochemicals and pharmaceuticals for potential liver toxicity is required for regulatory approval and is an expensive and time-consuming process. The identification and utilization of early exposure gene signatures and robust predictive models in regulatory toxicity testing has the potential to reduce time and costs substantially. In this study, comparative supervised machine learning approaches were applied to the rat liver TG-GATEs dataset to develop feature selection and predictive testing. We identified ten gene biomarkers using three different feature selection methods that predicted liver necrosis with high specificity and selectivity in an independent validation dataset from the Microarray Quality Control (MAQC)-II study. Nine of the ten genes that were selected with the

SUBMITTER: Smith BP 

PROVIDER: S-EPMC7645727 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature