Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds-Reference-Cited by-同舟云学术

Symbolic, Neural, and Bayesian Machine Learning Models for Predicting Carcinogenicity of Chemical Compounds

Published:2000-07-01 Issue:4 Volume:40 Page:906-914
ISSN:0095-2338
Container-title:Journal of Chemical Information and Computer Sciences
language:en
Short-container-title:J. Chem. Inf. Comput. Sci.

Author:

Bahler Dennis¹,Stone Brian¹,Wellington Carol²,Bristol Douglas W.³

Affiliation:

1. Artificial Intelligence Laboratory, Department of Computer Science, North Carolina State University, Raleigh, North Carolina 27695-8206

2. Department of Mathematics and Computer Science, Shippensburg University, Shippensburg, Pennsylvania 17257-2299

3. National Institute of Environmental Health Sciences, Research Triangle Park, North Carolina 27709

Publisher

American Chemical Society (ACS)

Subject

Computational Theory and Mathematics,Computer Science Applications,Information Systems,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/ci990116i

Reference40 articles.

1. Classification according to chemical structure, mutagenicity to Salmonella and level of carcinogenicity of a further 42 chemicals tested for carcinogenicity by the U.S. National Toxicology Program

2. Bahler, D.; Bristol, D. W. Prediction of Chemical Carcinogenicity in Rodents By Machine Learning of Decision Trees and Rule Sets.Predictive Toxicologyof Chemicals: Experiences and Impact of AI Tools; Papers from the 1999 AAAI Spring Symposium, Technical Report SS-99-01, AAAI Press: Menlo Park, CA, 1999; pp 74−77.

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