机器学习
人工智能
雄激素受体
兴奋剂
朴素贝叶斯分类器
计算机科学
受体
生物信息学
计算生物学
支持向量机
生物
医学
内科学
生物化学
前列腺癌
癌症
作者
Kimberley M. Zorn,Daniel H. Foil,Thomas R. Lane,Wendy Hillwalker,David J Feifarek,Frank E. Jones,William D Klaren,Ashley M Brinkman,Sean Ekins
标识
DOI:10.1021/acs.est.0c03984
摘要
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.
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