Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions

  • Chun-Wei Tung
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8626)


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.


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


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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Chun-Wei Tung
    • 1
    • 2
    • 3
  1. 1.School of PharmacyKaohsiung Medical UniversityTaiwan
  2. 2.Ph.D. Program in ToxicologyKaohsiung Medical UniversityTaiwan
  3. 3.National Environmental Health Research CenterNational Health Research InstitutesMiaoli CountyTaiwan

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