Acquiring decision rules for predicting ames-negative hepatocarcinogens using chemical-chemical interactions

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

7 Citations (Scopus)

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.

Original languageEnglish
Title of host publicationPattern Recognition in Bioinformatics - 9th IAPR International Conference, PRIB 2014, Proceedings
PublisherSpringer Verlag
Pages1-9
Number of pages9
ISBN (Print)9783319091914
DOIs
Publication statusPublished - Jan 1 2014
Externally publishedYes
Event9th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2014 - Stockholm, Sweden
Duration: Aug 21 2014Aug 23 2014

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8626 LNBI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference9th IAPR International Conference on Pattern Recognition in Bioinformatics, PRIB 2014
CountrySweden
CityStockholm
Period8/21/148/23/14

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Keywords

  • Ames-Negative Hepatocarcinogens
  • Chemical-Chemical Interaction
  • Decision Tree
  • Interpretable Rule
  • Toxicology

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Cite this

Tung, C. W. (2014). Acquiring decision rules for predicting ames-negative hepatocarcinogens using chemical-chemical interactions. In Pattern Recognition in Bioinformatics - 9th IAPR International Conference, PRIB 2014, Proceedings (pp. 1-9). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8626 LNBI). Springer Verlag. https://doi.org/10.1007/978-3-319-09192-1_1