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)

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.

Keywords

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

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References

  1. 1.
    Hayashi, Y.: Overview of genotoxic carcinogens and non-genotoxic carcinogens. Exp. Toxicol. Pathol. 44, 465–471 (1992)CrossRefGoogle Scholar
  2. 2.
    Weisburger, J.H., Williams, G.M.: The distinction between genotoxic and epigenetic carcinogens and implication for cancer risk. Toxicol. Sci. 57, 4–5 (2000)CrossRefGoogle Scholar
  3. 3.
    Zeiger, E.: Identification of rodent carcinogens and noncarcinogens using genetic toxicity tests: premises, promises, and performance. Regul. Toxicol. Pharmacol. 28, 85–95 (1998)CrossRefGoogle Scholar
  4. 4.
    Benigni, R., Bossa, C., Tcheremenskaia, O., Giuliani, A.: Alternatives to the carcinogenicity bioassay: in silico methods, and the in vitro and in vivo mutagenicity assays. Expert Opin. Drug Metab. Toxicol. 6, 809–819 (2010)CrossRefGoogle Scholar
  5. 5.
    Cunningham, A.R., Carrasquer, C.A., Qamar, S., Maguire, J.M., Cunningham, S.L., Trent, J.O.: Global structure-activity relationship model for nonmutagenic carcinogens using virtual ligand-protein interactions as model descriptors. Carcinogenesis 33, 1940–1945 (2012)CrossRefGoogle Scholar
  6. 6.
    Zeiger, E.: Historical perspective on the development of the genetic toxicity test battery in the united states. Environ. Mol. Mutagen. 51, 781–791 (2010)CrossRefGoogle Scholar
  7. 7.
    Liu, Z., Kelly, R., Fang, H., Ding, D., Tong, W.: Comparative analysis of predictive models for nongenotoxic hepatocarcinogenicity using both toxicogenomics and quantitative structure-activity relationships. Chem. Res. Toxicol. 24, 1062–1070 (2011)CrossRefGoogle Scholar
  8. 8.
    Yamada, F., Sumida, K., Uehara, T., Morikawa, Y., Yamada, H., Urushidani, T., Ohno, Y.: Toxicogenomics discrimination of potential hepatocarcinogenicity of non-genotoxic compounds in rat liver. J. Appl. Toxicol. (2012)Google Scholar
  9. 9.
    Tung, C.W.: Prediction of non-genotoxic hepatocarcinogenicity using chemical-protein interactions. In: Ngom, A., Formenti, E., Hao, J.-K., Zhao, X.-M., van Laarhoven, T. (eds.) PRIB 2013. LNCS, vol. 7986, pp. 231–241. Springer, Heidelberg (2013)CrossRefGoogle Scholar
  10. 10.
    Young, J., Tong, W., Fang, H., Xie, Q., Pearce, B., Hashemi, R., Beger, R., Cheeseman, M., Chen, J., Chang, Y.C., Kodell, R.: Building an organ-specific carcinogenic database for sar analyses. J. Toxicol. Environ. Health A 67, 1363–1389 (2004)CrossRefGoogle Scholar
  11. 11.
    Kuhn, M., Szklarczyk, D., Franceschini, A., von Mering, C., Jensen, L.J., Bork, P.: Stitch 3: zooming in on protein-chemical interactions. Nucleic Acids Res. 40, D876–D880 (2012)Google Scholar
  12. 12.
    Lu, J., Huang, G., Li, H.P., Feng, K.Y., Chen, L., Zheng, M.Y., Cai, Y.D.: Prediction of cancer drugs by chemical-chemical interactions. PLoS One 9, e87791 (2014)Google Scholar
  13. 13.
    Chen, L., Lu, J., Luo, X., Feng, K.Y.: Prediction of drug target groups based on chemical-chemical similarities and chemical-chemical/protein connections. Biochim. Biophys. Acta 1844, 207–213 (2014)CrossRefGoogle Scholar
  14. 14.
    Yap, C.W.: Padel-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32, 1466–1474 (2011)CrossRefGoogle Scholar
  15. 15.
    Tung, C.W., Ziehm, M., Kämper, A., Kohlbacher, O., Ho, S.Y.: Popisk: T-cell reactivity prediction using support vector machines and string kernels. BMC Bioinformatics 12, 446 (2011)CrossRefGoogle Scholar
  16. 16.
    Tung, C.W., Ho, S.Y.: Computational identification of ubiquitylation sites from protein sequences. BMC Bioinformatics 9, 310 (2008)CrossRefGoogle Scholar
  17. 17.
    Tung, C.W., Wu, M.T., Chen, Y.K., Wu, C.C., Chen, W.C., Li, H.P., Chou, S.H., Wu, D.C., Wu, I.C.: Identification of biomarkers for esophageal squamous cell carcinoma using feature selection and decision tree methods. Sci. World J. 2013, 782031 (2013)Google Scholar
  18. 18.
    Quinlan, J.: C4. 5: programs for machine learning (1993)Google Scholar
  19. 19.
    Kuhn, M., Weston, S.: Code for C5.0 by R. Quinlan, N.C.C.: C50: C5.0 Decision Trees and Rule-Based Models (2014); R package version 0.1.0-016Google Scholar
  20. 20.
    Tung, C.W.: Prediction of pupylation sites using the composition of k-spaced amino acid pairs. J. Theoretical Biol. 336, 11–17 (2013)CrossRefGoogle Scholar
  21. 21.
    Tung, C.W., Ho, S.Y.: Popi: predicting immunogenicity of mhc class i binding peptides by mining informative physicochemical properties. Bioinformatics 23, 942–949 (2007)CrossRefGoogle Scholar
  22. 22.
    De Jay, N., Papillon-Cavanagh, S., Olsen, C., El-Hachem, N., Bontempi, G., Haibe-Kains, B.: Mrmre: an r package for parallelized mrmr ensemble feature selection. Bioinformatics 29, 2365–2368 (2013)CrossRefGoogle Scholar

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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