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.
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Hayashi, Y.: Overview of genotoxic carcinogens and non-genotoxic carcinogens. Exp. Toxicol. Pathol. 44, 465–471 (1992)
Weisburger, J.H., Williams, G.M.: The distinction between genotoxic and epigenetic carcinogens and implication for cancer risk. Toxicol. Sci. 57, 4–5 (2000)
Zeiger, E.: Identification of rodent carcinogens and noncarcinogens using genetic toxicity tests: premises, promises, and performance. Regul. Toxicol. Pharmacol. 28, 85–95 (1998)
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)
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)
Zeiger, E.: Historical perspective on the development of the genetic toxicity test battery in the united states. Environ. Mol. Mutagen. 51, 781–791 (2010)
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)
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)
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)
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)
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)
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)
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)
Yap, C.W.: Padel-descriptor: an open source software to calculate molecular descriptors and fingerprints. J. Comput. Chem. 32, 1466–1474 (2011)
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)
Tung, C.W., Ho, S.Y.: Computational identification of ubiquitylation sites from protein sequences. BMC Bioinformatics 9, 310 (2008)
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)
Quinlan, J.: C4. 5: programs for machine learning (1993)
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 0.1.0-016
Tung, C.W.: Prediction of pupylation sites using the composition of k-spaced amino acid pairs. J. Theoretical Biol. 336, 11–17 (2013)
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)
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)
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Tung, CW. (2014). Acquiring Decision Rules for Predicting Ames-Negative Hepatocarcinogens Using Chemical-Chemical Interactions. In: Comin, M., Käll, L., Marchiori, E., Ngom, A., Rajapakse, J. (eds) Pattern Recognition in Bioinformatics. PRIB 2014. Lecture Notes in Computer Science(), vol 8626. Springer, Cham. https://doi.org/10.1007/978-3-319-09192-1_1
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DOI: https://doi.org/10.1007/978-3-319-09192-1_1
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