Prediction of non-genotoxic hepatocarcinogenicity using chemical-protein interactions

Research output: Chapter in Book/Report/Conference proceedingConference contribution

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013, Proceedings
Pages231-241
Number of pages11
DOIs
Publication statusPublished - Aug 1 2013
Externally publishedYes
Event8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013 - Nice, France
Duration: Jun 17 2013Jun 20 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7986 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2013
CountryFrance
CityNice
Period6/17/136/20/13

Keywords

  • Chemical-Protein Interaction
  • Decision Tree
  • Interpretable Rule
  • Non-Genotoxic Hepatocarcinogenicity
  • Toxicology

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Fingerprint Dive into the research topics of 'Prediction of non-genotoxic hepatocarcinogenicity using chemical-protein interactions'. Together they form a unique fingerprint.

  • Cite this

    Tung, C. W. (2013). Prediction of non-genotoxic hepatocarcinogenicity using chemical-protein interactions. In Pattern Recognition in Bioinformatics - 8th IAPR International Conference, PRIB 2013, Proceedings (pp. 231-241). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7986 LNBI). https://doi.org/10.1007/978-3-642-39159-0-21