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题名: A systematic identification of multiple toxin-target interactions based on chemical, genomic and toxicological data
作者: Zhou, Wei1;  Huang, Chao1;  Li, Yan2;  Duan, Jinyou3;  Wang, Yonghua1;  Yang, Ling4
关键词: Multiple toxin-target interactions ;  In silico prediction ;  SVM ;  RF ;  Network toxicology
刊名: TOXICOLOGY
发表日期: 2013-02-08
DOI: 10.1016/j.tox.2012.12.012
卷: 304, 页:173-184
收录类别: SCI
文章类型: Article
WOS标题词: Science & Technology ;  Life Sciences & Biomedicine
类目[WOS]: Pharmacology & Pharmacy ;  Toxicology
研究领域[WOS]: Pharmacology & Pharmacy ;  Toxicology
英文摘要: 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. Crown Copyright (C) 2012 Published by Elsevier Ireland Ltd. All rights reserved.
关键词[WOS]: MOLECULAR DOCKING ;  IN-SILICO ;  PREDICTION ;  NETWORKS ;  CLASSIFICATION ;  INTEGRATION ;  PARAMETERS ;  TOXICITY ;  FEATURES ;  BINDING
语种: 英语
WOS记录号: WOS:000316522300019
Citation statistics: 
内容类型: 期刊论文
URI标识: http://cas-ir.dicp.ac.cn/handle/321008/137756
Appears in Collections:中国科学院大连化学物理研究所_期刊论文

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作者单位: 1.Northwest A&F Univ, Coll Life Sci, Yangling 712100, Shaanxi, Peoples R China
2.Dalian Univ Technol, Sch Chem Engn, Dalian 116024, Liaoning, Peoples R China
3.Northwest A&F Univ, Coll Sci, Yangling 712100, Shaanxi, Peoples R China
4.Chinese Acad Sci, Dalian Inst Chem Phys, Lab Pharmaceut Resource Discovery, Dalian 116023, Liaoning, Peoples R China

Recommended Citation:
Zhou, Wei,Huang, Chao,Li, Yan,et al. A systematic identification of multiple toxin-target interactions based on chemical, genomic and toxicological data[J]. TOXICOLOGY,2013,304:173-184.
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