Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions-Reference-Cited by-同舟云学术

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Advancing toxicity studies of per- and poly-fluoroalkyl substances (pfass) through machine learning: Models, mechanisms, and future directions

Published:2024-10 Issue: Volume:946 Page:174201
ISSN:0048-9697
Container-title:Science of The Total Environment
language:en
Short-container-title:Science of The Total Environment

Author:

Meng Lingxuan,Zhou Beihai,Liu Haijun,Chen Yuefang,Yuan Rongfang,Chen Zhongbing,Luo Shuai,Chen Huilun

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Reference165 articles.

1. Modeling the transplacental transfer of small molecules using machine learning: a case study on per- and polyfluorinated substances (PFAS);Abrahamsson;J. Expo. Sci. Environ. Epidemiol.,2022

2. A machine learning-based approach to ERα bioactivity and drug ADMET prediction;An;Front. Genet.,2023

3. Computational models using multiple machine learning algorithms for predicting drug hepatotoxicity with the DILIrank data- set;Ancuceanu;Int. J. Mol. Sci.,2020

4. Adverse outcome pathways: a conceptual framework to support ecotoxicology research and risk assessment;Ankley;Environ. Toxicol. Chem.,2010

5. Assessing the ecological risks of per- and Polyfluoroalkyl substances: current state-of-the science and a proposed path forward;Ankley;Environ. Toxicol. Chem.,2020

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