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Issue 6, 2013
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Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs)

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Abstract

New technologies for systems-level determinants of human exposure to drugs, industrial chemicals, pesticides, and other environmental agents provide an invaluable opportunity to extend the understanding of human health and potential environmental hazards. We report here the development of a new computational-systems toxicology framework, called predictive toxicogenomics-derived models (PTDMs). PTDMs integrate three networks of chemical–gene interactions (CGIs), chemical–disease associations (CDAs) and gene–disease associations (GDAs) to infer chemical hazard profiles, identify exposure data gaps and to incorporate genes and disease networks into chemical safety evaluations. Three comprehensive networks addressing CGI, CDA and GDA extracted from the comparative toxicogenomics database (CTD) were constructed. The areas under the receiver operating characteristics curve ranged from 0.85 to 0.97 and were yielded using our methodology using a 10-fold cross validation by a simulation carried out 100 times. As the illustrated examples show, we predicted new potential target genes and diseases for bisphenol A and aspirin. The molecular hypothesis and experimental evidence from published literature for these predictions were provided. The results demonstrated that our method has potential applications for chemical profiling in human health exposure and environmental hazard assessment.

Graphical abstract: Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs)

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Publication details

The article was received on 03 Aug 2012, accepted on 01 Feb 2013 and first published on 04 Feb 2013


Article type: Paper
DOI: 10.1039/C3MB25309K
Citation: Mol. BioSyst., 2013,9, 1316-1325
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    Prediction of human genes and diseases targeted by xenobiotics using predictive toxicogenomic-derived models (PTDMs)

    F. Cheng, W. Li, Y. Zhou, J. Li, J. Shen, P. W. Lee and Y. Tang, Mol. BioSyst., 2013, 9, 1316
    DOI: 10.1039/C3MB25309K

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