Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information-Reference-Cited by-同舟云学术

Systematically evaluating read-across prediction and performance using a local validity approach characterized by chemical structure and bioactivity information

Published:2016-08 Issue: Volume:79 Page:12-24
ISSN:0273-2300
Container-title:Regulatory Toxicology and Pharmacology
language:en
Short-container-title:Regulatory Toxicology and Pharmacology

Author:

Shah Imran^ORCID,Liu Jie,Judson Richard S.^ORCID,Thomas Russell S.,Patlewicz Grace

Funder

U.S. Environmental Protection Agency

Office of Research and Development

Oak Ridge Institute for Science and Education

U.S. Department of Energy

Publisher

Elsevier BV

Subject

Toxicology,General Medicine

Reference39 articles.

1. Instance-based learning algorithms;Aha;Mach. Learn.,1991

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

3. The challenge of using read-across within the EU REACH regulatory framework; how much uncertainty is too much? Dipropylene glycol methyl ether acetate, an exemplary case study;Ball;Regul. Toxicol. Pharmacol.,2014

4. Animal use and lessons learned in the U.S. high production volume chemicals challenge program;Bishop;Environ. Health Perspect.,2012

5. A framework to facilitate consistent characterization of read across uncertainty;Blackburn;Regul. Toxicol. Pharmacol.,2014

Cited by 72 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

2. Assessing human carcinogenicity risk of agrochemicals without the rodent cancer bioassay;Frontiers in Toxicology;2024-06-12

3. A systematic analysis of read-across within REACH registration dossiers;Computational Toxicology;2024-06

4. MetSim: Integrated Programmatic Access and Pathway Management for Xenobiotic Metabolism Simulators;Chemical Research in Toxicology;2024-04-10

5. A comparison of machine learning approaches for predicting hepatotoxicity potential using chemical structure and targeted transcriptomic data;Computational Toxicology;2024-03