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Conference Paper: Metabolite biomarker discovery for metabolic diseases by flux analysis

TitleMetabolite biomarker discovery for metabolic diseases by flux analysis
Authors
KeywordsBio-marker discovery
Biochemical network
Biochemical reactions
Biomedical data
Flux analysis
Issue Date2012
PublisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515
Citation
The 6th IEEE International Conference on Systems Biology (ISB 2012), Xian China, 18-20 August 2012, In IEEE International Conference on Systems Biology Proceedings, 2012, p. 1-5 How to Cite?
AbstractMetabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers. © 2012 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/165342
ISBN

 

DC FieldValueLanguage
dc.contributor.authorLi, Len_US
dc.contributor.authorJiang, Hen_US
dc.contributor.authorChing, WKen_US
dc.contributor.authorVassiliadis, VSen_US
dc.date.accessioned2012-09-20T08:17:29Z-
dc.date.available2012-09-20T08:17:29Z-
dc.date.issued2012en_US
dc.identifier.citationThe 6th IEEE International Conference on Systems Biology (ISB 2012), Xian China, 18-20 August 2012, In IEEE International Conference on Systems Biology Proceedings, 2012, p. 1-5en_US
dc.identifier.isbn978-1-4673-4398-5-
dc.identifier.urihttp://hdl.handle.net/10722/165342-
dc.description.abstractMetabolites can serve as biomarkers and their identification has significant importance in the study of biochemical reaction and signalling networks. Incorporating metabolic and gene expression data to reveal biochemical networks is a considerable challenge, which attracts a lot of attention in recent research. In this paper, we propose a promising approach to identify metabolic biomarkers through integrating available biomedical data and disease-specific gene expression data. A Linear Programming (LP) based method is then utilized to determine flux variability intervals, therefore enabling the analysis of significant metabolic reactions. A statistical approach is also presented to uncover these metabolites. The identified metabolites are then verified by comparing with the results in the existing literature. The proposed approach here can also be applied to the discovery of potential novel biomarkers. © 2012 IEEE.-
dc.languageengen_US
dc.publisherIEEE. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/conhome.jsp?punumber=1800515-
dc.relation.ispartofIEEE International Conference on Systems Biology Proceedingsen_US
dc.rightsIEEE International Conference on Systems Biology Proceedings. Copyright © IEEE.-
dc.rights©2012 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectBio-marker discovery-
dc.subjectBiochemical network-
dc.subjectBiochemical reactions-
dc.subjectBiomedical data-
dc.subjectFlux analysis-
dc.titleMetabolite biomarker discovery for metabolic diseases by flux analysisen_US
dc.typeConference_Paperen_US
dc.identifier.emailLi, L: liminli@HKUSUA.hku.hken_US
dc.identifier.emailJiang, H: haohao@HKUSUC.hku.hk-
dc.identifier.emailChing, WK: wching@hku.hk-
dc.identifier.authorityChing, WK=rp00679en_US
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ISB.2012.6314103-
dc.identifier.scopuseid_2-s2.0-84868634268-
dc.identifier.hkuros207592en_US
dc.identifier.spage1en_US
dc.identifier.epage5en_US
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 130325-

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