Predicting molecular initiating events using chemical target annotations and gene expression.

ABSTRACT:

Background

The advent of high-throughput transcriptomic screening technologies has resulted in a wealth of publicly available gene expression data associated with chemical treatments. From a regulatory perspective, data sets that cover a large chemical space and contain reference chemicals offer utility for the prediction of molecular initiating events associated with chemical exposure. Here, we integrate data from a large compendium of transcriptomic responses to chemical exposure with a comprehensive database of chemical-protein associations to train binary classifiers that predict mechanism(s) of action from transcriptomic responses. First, we linked reference chemicals present in the LINCS L1000 gene expression data collection to chemical identifiers in RefChemDB, a database of

SUBMITTER: Bundy JL 

PROVIDER: S-EPMC8895536 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature