Machine Learning Models for Estrogen Receptor Bioactivity and Endocrine Disruption Prediction.

ABSTRACT: The U.S. Environmental Protection Agency (EPA) periodically releases in vitro data across a variety of targets, including the estrogen receptor (ER). In 2015, the EPA used these data to construct mathematical models of ER agonist and antagonist pathways to prioritize chemicals for endocrine disruption testing. However, mathematical models require in vitro data prior to predicting estrogenic activity, but machine learning methods are capable of prospective prediction from the molecular structure alone. The current study describes the generation and evaluation of Bayesian machine learning models grouped by the EPA's ER agonist pathway model using multiple data types with proprietary software, Assay Central. External predictions with three test sets of in vitro and in

SUBMITTER: Zorn KM 

PROVIDER: S-EPMC8194504 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature