@inproceedings{5736ad64ccf1443396395737944d9ce2,
title = "Acquiring decision rules for predicting ames-negative hepatocarcinogens using chemical-chemical interactions",
abstract = "Chemical carcinogenicity is an important safety issue for the evaluation of drugs and environmental pollutants. The Ames test is useful for detecting genotoxic hepatocarcinogens. However, the assessment of Ames-negative hepatocarcinogens depends on 2-year rodent bioassays. Alternative methods are desirable for the efficient identification of Ames-negative hepatocarcinogens. This study proposed a decision tree-based method using chemical-chemical interaction information for predicting hepatocarcinogens. It performs much better than that using molecular descriptors with accuracies of 86% and 76% for validation and independent test, respectively. Four important interacting chemicals with interpretable decision rules were identified and analyzed. With the high prediction performances, the acquired decision rules based on chemical-chemical interactions provide a useful prediction method and better understanding of Ames-negative hepatocarcinogens.",
keywords = "Ames-Negative Hepatocarcinogens, Chemical-Chemical Interaction, Decision Tree, Interpretable Rule, Toxicology",
author = "Tung, {Chun Wei}",
year = "2014",
month = jan,
day = "1",
doi = "10.1007/978-3-319-09192-1_1",
language = "English",
isbn = "9783319091914",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "1--9",
booktitle = "Pattern Recognition in Bioinformatics - 9th IAPR International Conference, PRIB 2014, Proceedings",
address = "Germany",
note = "9th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2014 ; Conference date: 21-08-2014 Through 23-08-2014",
}