@inproceedings{7002f2949e4542cc8baed5c30e750673,
title = "Prediction of non-genotoxic hepatocarcinogenicity using chemical-protein interactions",
abstract = "The assessment of non-genotoxic hepatocarcinogenicity of chemicals is currently based on 2-year rodent bioassays. It is desirable to develop a fast and effective method to accelerate the identification of potential hepatocarcinogenicity of non-genotoxic chemicals. In this study, a novel method CPI is proposed to predict potential hepatocarcinogenicity of non-genotoxic chemicals. The CPI method is based on chemical-protein interactions and interpretable decision tree classifiers.The interpretable rules generated by the CPI method are analyzed to provide insights into the mechanism and biomarkers of non-genotoxic hepatocarcinogenicity. The CPI method with an independent test accuracy of 86% using only 1 protein biomarker outperforms the state-of-the-art methods of gene expression profile-based toxicogenomics using 90 gene biomarkers. A protein ABCC3 was identified as a potential protein biomarker for further exploration. This study presents the potential application of CPI method for assessing non-genotoxic hepatocarcinogenicity of chemicals.",
keywords = "Chemical-Protein Interaction, Decision Tree, Interpretable Rule, Non-Genotoxic Hepatocarcinogenicity, Toxicology",
author = "Tung, {Chun Wei}",
year = "2013",
month = aug,
day = "1",
doi = "10.1007/978-3-642-39159-0-21",
language = "English",
isbn = "9783642391583",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "231--241",
booktitle = "Pattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013, Proceedings",
note = "8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013 ; Conference date: 17-06-2013 Through 20-06-2013",
}