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Database

The Predicted Chemical-Gene-Disease Associations Database

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 environmental hazard potential. We reported here the development of a new systems toxicology framework, called predictive toxicogenomics-derived models (PTDMs). PTDMs integrate the networks of chemical-gene interactions (CGI), chemical-disease associations (CDA) and gene-disease associations (GDA) to infer chemical hazard profiles, identify exposure data gaps and to incorporate genes and diseases network into chemical safety evaluation. Three comprehensive networks addressing CGI, CDA and GDA extracted from the comparative toxicogenomics database (CTD) were constructed. The area under of the receiver operating characteristics curve ranged from 0.85 to 0.97 in 10-fold cross validation by 100 times simulation was achieved using our methodology. As illustrating examples, we predicted new potential genes and diseases for bisphenol A and aspirin. The molecular hypothesis and experimental evidences from literatures for these predictions were provided. We also presented a comprehensive predicted database of chemical-gene-disease associations (PredCTD). This new network method provides quantitative guidelines for chemical screening at toxicogenomics level in human health exposure and environmental hazard assessment.

Experimental Data sets

Predicted Chemical-Gene-Disease Associations

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Reference

Feixiong Cheng, Weihua Li, Yadi Zhou, Jie Li, Jie Shen, Philip W. Lee, Yun Tang*. Prediction of Human Genes and Diseases Targeted by Xenobiotics Using Predictive Toxicogenomics-Derived Models (PTDMs). Mol. BioSyst, 2013, 9(6): 1316-1325.

Feixiong Cheng, Chuang Liu, Jing Jiang, Weiqiang Lu, Weihua Li, Guixia Liu, Weixing Zhou, Jin Huang, and Yun Tang*. Prediction of Drug-Target Interactions and Drug Repositioning via Network-Based Inference. PLoS Comput Biol, 2012, 8(5): e1002503.