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Article: Comparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification

TitleComparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identification
Authors
Issue Date2008
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjbin
Citation
Journal Of Biomedical Informatics, 2008, v. 41 n. 2, p. 272-281 How to Cite?
AbstractComputational identification of missing enzymes plays a significant role in accurate and complete reconstruction of metabolic network for both newly sequenced and well-studied organisms. For a metabolic reaction, given a set of candidate enzymes identified according to certain biological evidences, a powerful mathematical model is required to predict the actual enzyme(s) catalyzing the reactions. In this study, several plausible predictive methods are considered for the classification problem in missing enzyme identification, and comparisons are performed with an aim to identify a method with better performance than the Bayesian model used in previous work. In particular, a regression model consisting of a linear term and a nonlinear term is proposed to apply to the problem, in which the reversible jump Markov-chain-Monte-Carlo (MCMC) learning technique (developed in [Andrieu C, Freitas Nando de, Doucet A. Robust full Bayesian learning for radial basis networks 2001;13:2359-407.]) is adopted to estimate the model order and the parameters. We evaluated the models using known reactions in Escherichia coli, Mycobacterium tuberculosis, Vibrio cholerae and Caulobacter cresentus bacteria, as well as one eukaryotic organism, Saccharomyces Cerevisiae. Although support vector regression also exhibits comparable performance in this application, it was demonstrated that the proposed model achieves favorable prediction performance, particularly sensitivity, compared with the Bayesian method. © 2007 Elsevier Inc. All rights reserved.
Persistent Identifierhttp://hdl.handle.net/10722/74110
ISSN
2015 Impact Factor: 2.447
2015 SCImago Journal Rankings: 1.283
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorGeng, Ben_HK
dc.contributor.authorZhou, Xen_HK
dc.contributor.authorZhu, Jen_HK
dc.contributor.authorHung, YSen_HK
dc.contributor.authorWong, STCen_HK
dc.date.accessioned2010-09-06T06:57:54Z-
dc.date.available2010-09-06T06:57:54Z-
dc.date.issued2008en_HK
dc.identifier.citationJournal Of Biomedical Informatics, 2008, v. 41 n. 2, p. 272-281en_HK
dc.identifier.issn1532-0464en_HK
dc.identifier.urihttp://hdl.handle.net/10722/74110-
dc.description.abstractComputational identification of missing enzymes plays a significant role in accurate and complete reconstruction of metabolic network for both newly sequenced and well-studied organisms. For a metabolic reaction, given a set of candidate enzymes identified according to certain biological evidences, a powerful mathematical model is required to predict the actual enzyme(s) catalyzing the reactions. In this study, several plausible predictive methods are considered for the classification problem in missing enzyme identification, and comparisons are performed with an aim to identify a method with better performance than the Bayesian model used in previous work. In particular, a regression model consisting of a linear term and a nonlinear term is proposed to apply to the problem, in which the reversible jump Markov-chain-Monte-Carlo (MCMC) learning technique (developed in [Andrieu C, Freitas Nando de, Doucet A. Robust full Bayesian learning for radial basis networks 2001;13:2359-407.]) is adopted to estimate the model order and the parameters. We evaluated the models using known reactions in Escherichia coli, Mycobacterium tuberculosis, Vibrio cholerae and Caulobacter cresentus bacteria, as well as one eukaryotic organism, Saccharomyces Cerevisiae. Although support vector regression also exhibits comparable performance in this application, it was demonstrated that the proposed model achieves favorable prediction performance, particularly sensitivity, compared with the Bayesian method. © 2007 Elsevier Inc. All rights reserved.en_HK
dc.languageengen_HK
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/yjbinen_HK
dc.relation.ispartofJournal of Biomedical Informaticsen_HK
dc.subject.meshAlgorithmsen_HK
dc.subject.meshArtificial Intelligenceen_HK
dc.subject.meshComputer Simulationen_HK
dc.subject.meshGene Expression Profiling - methodsen_HK
dc.subject.meshModels, Biologicalen_HK
dc.subject.meshModels, Statisticalen_HK
dc.subject.meshMonte Carlo Methoden_HK
dc.subject.meshMultienzyme Complexes - metabolismen_HK
dc.subject.meshPattern Recognition, Automated - methodsen_HK
dc.subject.meshSignal Transduction - physiologyen_HK
dc.titleComparison of reversible-jump Markov-chain-Monte-Carlo learning approach with other methods for missing enzyme identificationen_HK
dc.typeArticleen_HK
dc.identifier.openurlhttp://library.hku.hk:4550/resserv?sid=HKU:IR&issn=1532-0464&volume=41&spage=272&epage=281&date=2008&atitle=Comparison+of+reversible-jump+Markov-chain-Monte-Carlo+learning+approach+with+other+methods+for+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.doi10.1016/j.jbi.2007.09.002en_HK
dc.identifier.pmid17950040-
dc.identifier.scopuseid_2-s2.0-40049107547en_HK
dc.identifier.hkuros149092en_HK
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-40049107547&selection=ref&src=s&origin=recordpageen_HK
dc.identifier.volume41en_HK
dc.identifier.issue2en_HK
dc.identifier.spage272en_HK
dc.identifier.epage281en_HK
dc.identifier.isiWOS:000255360000007-
dc.publisher.placeUnited Statesen_HK
dc.identifier.scopusauthoridGeng, B=25641387700en_HK
dc.identifier.scopusauthoridZhou, X=8914487400en_HK
dc.identifier.scopusauthoridZhu, J=7405689177en_HK
dc.identifier.scopusauthoridHung, YS=8091656200en_HK
dc.identifier.scopusauthoridWong, STC=24726534400en_HK
dc.identifier.citeulike2693511-

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