國家衛生研究院 NHRI:Item 3990099045/8872
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    題名: ChemDIS: A chemical-disease inference system based on chemical-protein interactions
    作者: Tung, CW
    貢獻者: National Institute of Environmental Health Sciences
    摘要: BACKGROUND:The characterization of toxicities associated with environmental and industrial chemicals is required for risk assessment. However, we lack the toxicological data for a large portion of chemicals due to the high cost of experiments for a huge number of chemicals. The development of computational methods for identifying potential risks associated with chemicals is desirable for generating testable hypothesis to accelerate the hazard identification process.RESULTS:A chemical-disease inference system named ChemDIS was developed to facilitate hazard identification for chemicals. The chemical-protein interactions from a large database STITCH and protein-disease relationship from disease ontology and disease ontology lite were utilized for chemical-protein-disease inferences. Tools with user-friendly interfaces for enrichment analysis of functions, pathways and diseases were implemented and integrated into ChemDIS. An analysis on maleic acid and sibutramine showed that ChemDIS could be a useful tool for the identification of potential functions, pathways and diseases affected by poorly characterized chemicals.CONCLUSIONS:ChemDIS is an integrated chemical-disease inference system for poorly characterized chemicals with potentially affected functions and pathways for experimental validation.ChemDIS server is freely accessible at http://cwtung.kmu.edu.tw/chemdis.
    日期: 2015-06-15
    關聯: Journal of Cheminformatics. 2015 Jun 15;7(1):Article number 25.
    Link to: http://dx.doi.org/10.1186/s13321-015-0077-3
    JIF/Ranking 2023: http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcAuth=NHRI&SrcApp=NHRI_IR&KeyISSN=1758-2946&DestApp=IC2JCR
    Cited Times(WOS): https://www.webofscience.com/wos/woscc/full-record/WOS:000356055400001
    Cited Times(Scopus): http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=84949133280
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