Comparison of Machine Learning Models for the Androgen Receptor.
Environ Sci Technol
; 54(21): 13690-13700, 2020 11 03.
Article
em En
| MEDLINE
| ID: mdl-33085465
ABSTRACT
The androgen receptor (AR) is a target of interest for endocrine disruption research, as altered signaling can affect normal reproductive and neurological development for generations. In an effort to prioritize compounds with alternative methodologies, the U.S. Environmental Protection Agency (EPA) used in vitro data from 11 assays to construct models of AR agonist and antagonist signaling pathways. While these EPA ToxCast AR models require in vitro data to assign a bioactivity score, Bayesian machine learning methods can be used for prospective prediction from molecule structure alone. This approach was applied to multiple types of data corresponding to the EPA's AR signaling pathway with proprietary software, Assay Central. The training performance of all machine learning models, including six other algorithms, was evaluated by internal 5-fold cross-validation statistics. Bayesian machine learning models were also evaluated with external predictions of reference chemicals to compare prediction accuracies to published results from the EPA. The machine learning model group selected for further studies of endocrine disruption consisted of continuous AC50 data from the February 2019 release of ToxCast/Tox21. These efforts demonstrate how machine learning can be used to predict AR-mediated bioactivity and can also be applied to other targets of endocrine disruption.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Receptores Androgênicos
/
Aprendizado de Máquina
Tipo de estudo:
Observational_studies
/
Prognostic_studies
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Risk_factors_studies
País como assunto:
America do norte
Idioma:
En
Ano de publicação:
2020
Tipo de documento:
Article