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Article: Comparison of Bayesian and regression models in missing enzyme identification

TitleComparison of Bayesian and regression models in missing enzyme identification
Authors
Geng, BZhou, XHung, YSWong, S
KeywordsBayesian model
Metabolic network
Missing enzymes identification
Regression
Issue Date2008
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra
Citation
International Journal Of Bioinformatics Research And Applications, 2008, v. 4 n. 4, p. 363-374 How to Cite?
DOI: http://dx.doi.org/10.1504/IJBRA.2008.021174
AbstractComputational identification of missing enzymes is important in metabolic network reconstruction. For a metabolic reaction, given a set of candidate enzymes identified by biological evidences, a powerful predictive model is necessary to predict the actual enzyme(s) catalysing the reaction. In this study, we compare Bayesian Method, which is used in previous work, with several regression models. We apply the models to known reactions in E. coli and three other bacteria. It is shown that the proposed regression models obtain favourable performance when compared with the Bayesian method. Copyright © 2008 Inderscience Enterprises Ltd.
Persistent Identifierhttp://hdl.handle.net/10722/58857
ISSN
1744-5485
2020 SCImago Journal Rankings: 0.109
References
References in Scopus

 

DC FieldValueLanguage
dc.contributor.authorGeng, Ben_HK
dc.contributor.authorZhou, Xen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorWong, Sen_HK
dc.date.accessioned2010-05-31T03:38:07Z-
dc.date.available2010-05-31T03:38:07Z-
dc.date.issued2008en_HK
dc.identifier.citationInternational Journal Of Bioinformatics Research And Applications, 2008, v. 4 n. 4, p. 363-374en_HK
dc.identifier.issn1744-5485en_HK
dc.identifier.urihttp://hdl.handle.net/10722/58857-
dc.description.abstractComputational identification of missing enzymes is important in metabolic network reconstruction. For a metabolic reaction, given a set of candidate enzymes identified by biological evidences, a powerful predictive model is necessary to predict the actual enzyme(s) catalysing the reaction. In this study, we compare Bayesian Method, which is used in previous work, with several regression models. We apply the models to known reactions in E. coli and three other bacteria. It is shown that the proposed regression models obtain favourable performance when compared with the Bayesian method. Copyright © 2008 Inderscience Enterprises Ltd.en_HK
dc.languageengen_HK
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbraen_HK
dc.relation.ispartofInternational Journal of Bioinformatics Research and Applicationsen_HK
dc.rightsInternational Journal of Bioinformatics Research and Applications. Copyright © Inderscience Publishers.en_HK
dc.subjectBayesian model-
dc.subjectMetabolic network-
dc.subjectMissing enzymes identification-
dc.subjectRegression-
dc.subject.meshArtificial Intelligenceen_HK
dc.subject.meshBayes Theoremen_HK
dc.subject.meshComputational Biologyen_HK
dc.subject.meshDatabases, Geneticen_HK
dc.subject.meshEnzymes - geneticsen_HK
dc.subject.meshMetabolic Networks and Pathwaysen_HK
dc.subject.meshRegression Analysisen_HK
dc.titleComparison of Bayesian and regression models in missing enzyme identificationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1744-5485&volume=4 No. 4&spage=363&epage=374&date=2008&atitle=Comparison+of+Bayesian+and+regression+models+in+missing+enzyme+identificationen_HK
dc.identifier.emailHung, YS:yshung@eee.hku.hken_HK
dc.identifier.authorityHung, YS=rp00220en_HK
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1504/IJBRA.2008.021174en_HK
dc.identifier.pmid19008181-
dc.identifier.scopuseid_2-s2.0-55849093649en_HK
dc.identifier.hkuros163896en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-55849093649&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume4en_HK
dc.identifier.issue4en_HK
dc.identifier.spage363en_HK
dc.identifier.epage374en_HK
dc.publisher.placeUnited Kingdomen_HK
dc.identifier.scopusauthoridGeng, B=25641387700en_HK
dc.identifier.scopusauthoridZhou, X=8914487400en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridWong, S=24726534400en_HK
dc.identifier.issnl1744-5485-

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