title: A systematic identification of multiple toxin-target interactions based on chemical, genomic and toxicological data.
abstract: Although the assessment of toxicity of various agents, -omics (genomic, proteomic, metabolomic, etc.) data has been accumulated largely, the acquirement of toxicity information of variety of molecules through experimental methods still remains a difficult task. Presently, a systems toxicology approach that integrates massive diverse chemical, genomic and toxicological information was developed for prediction of the toxin targets and their related networks. The procedures are: (1) by use of two powerful statistical methods, i.e., support vector machine (SVM) and random forest (RF), a systemic model for prediction of multiple toxin-target interactions using the extracted chemical and genomic features has been developed with its reliability and robustness estimated. And the qualitative classification of targets according to the phenotypic diseases has been taken into account to further uncover the biological meaning of the targets, as well as to validate the robustness of the in silico models. (2) Based on the predicted toxin-target interactions, a genome-scale toxin-target-disease network exampled by cardiovascular disease is generated. (3) A topological analysis of the network is carried out to identify those targets that are most susceptible in human to topical agents including the most critical toxins, as well as to uncover both the toxin-specific mechanisms and pathways. The methodologies presented herein for systems toxicology will make drug development, toxin environmental risk assessment more efficient, acceptable and cost-effective.
authors: College of Life Science, Northwest A&F University, Yangling, Shaanxi 712100, China.
authors: Wei
authors: W
authors: Zhou
authors: Chao
authors: C
authors: Huang
authors: Yan
authors: Y
authors: Li
authors: Jinyou
authors: J
authors: Duan
authors: Yonghua
authors: Y
authors: Wang
authors: Ling
authors: L
authors: Yang
mesh_terms: D019295:Computational Biology / Q000379:methods; D003198:Computer Simulation; D015195:Drug Design; D004781:Environmental Exposure / Q000009:adverse effects; D006801:Humans; D008958:Models, Molecular; D015233:Models, Statistical; D015203:Reproducibility of Results; D018570:Risk Assessment / Q000379:methods; D060388:Support Vector Machine; D014116:Toxicology / Q000379:methods*