Prediction of non-genotoxic hepatocarcinogenicity using chemical-protein interactions

Chun Wei Tung

研究成果: 書貢獻/報告類型會議貢獻

9 引文 斯高帕斯(Scopus)

摘要

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.

原文英語
主出版物標題Pattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013, Proceedings
頁面231-241
頁數11
DOIs
出版狀態已發佈 - 8月 1 2013
對外發佈
事件8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013 - Nice, 法国
持續時間: 6月 17 20136月 20 2013

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
7986 LNBI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

會議

會議8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013
國家/地區法国
城市Nice
期間6/17/136/20/13

ASJC Scopus subject areas

  • 電腦科學(全部)
  • 理論電腦科學

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