Comparison of Machine Learning Models for the Androgen Receptor-Reference-Cited by-同舟云学术

Comparison of Machine Learning Models for the Androgen Receptor

Published:2020-10-21 Issue:21 Volume:54 Page:13690-13700
ISSN:0013-936X
Container-title:Environmental Science & Technology
language:en
Short-container-title:Environ. Sci. Technol.

Author:

Zorn Kimberley M.¹,Foil Daniel H.¹,Lane Thomas R.¹,Hillwalker Wendy²,Feifarek David J.²,Jones Frank²,Klaren William D.²,Brinkman Ashley M.²,Ekins Sean¹^ORCID

Affiliation:

1. Collaborations Pharmaceuticals Inc., 840 Main Campus Drive, Lab 3510, Raleigh, North Carolina 27606, United States

2. Global Product Safety, SC Johnson and Son, Inc., Racine, Wisconsin 53404, United States

Funder

National Institute of Environmental Health Sciences

National Institute of General Medical Sciences

Publisher

American Chemical Society (ACS)

Subject

Environmental Chemistry,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.est.0c03984

Reference50 articles.

1. Use of High Throughput Assays and Computational Tools: Endocrine Disruptor Screening Program; Notice of Availability and Opportunity for Comment, 80 Fed. Reg. 118; U.S. EPA, 2015.

2. Biological Actions of Androgens

3. NORD National Organization for Rare Diseases. https://rarediseases.org.

4. The role of estrogen and androgen receptors in bone health and disease

5. Endocrine disrupting chemicals and disease susceptibility

Cited by 18 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Occurrence and Prioritization of Human Androgen Receptor Disruptors in Sewage Sludges Across China;Environmental Science & Technology;2024-05-25

2. CatNet: Sequence-based deep learning with cross-attention mechanism for identifying endocrine-disrupting chemicals;Journal of Hazardous Materials;2024-03

3. Comparative Study of Machine Learning-Based QSAR Modeling of Anti-inflammatory Compounds from Durian Extraction;ACS Omega;2024-02-07

4. Toward a comprehensive understanding of alicyclic compounds: Bio-effects perspective and deep learning approach;Science of The Total Environment;2024-02

5. Artificial intelligence-based prediction of hydrogen adsorption in various kerogen types: Implications for underground hydrogen storage and cleaner production;International Journal of Hydrogen Energy;2024-02