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

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TitleComparison of Bayesian and regression models in missing enzyme identification
AuthorsGeng, B1 2
Zhou, X2
Hung, YS1
Wong, S2
Issue Date2008
PublisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra
CitationInternational 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.
ISSN1744-5485
2011 SCImago Journal Rankings: 0.069
DOIhttp://dx.doi.org/10.1504/IJBRA.2008.021174
ReferencesReferences in Scopus
DC Field
Value
dc.contributor.authorGeng, B
dc.contributor.authorZhou, X
dc.contributor.authorHung, YS
dc.contributor.authorWong, S
dc.date.accessioned2010-05-31T03:38:07Z
dc.date.available2010-05-31T03:38:07Z
dc.date.issued2008
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.
dc.description.natureLink_to_subscribed_fulltext
dc.identifier.citationInternational 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
dc.identifier.doihttp://dx.doi.org/10.1504/IJBRA.2008.021174
dc.identifier.epage374
dc.identifier.hkuros163896
dc.identifier.issn1744-5485
2011 SCImago Journal Rankings: 0.069
dc.identifier.issue4
dc.identifier.openurl
dc.identifier.pmid19008181
dc.identifier.scopuseid_2-s2.0-55849093649
dc.identifier.spage363
dc.identifier.urihttp://hdl.handle.net/10722/58857
dc.identifier.volume4
dc.languageeng
dc.publisherInderscience Publishers. The Journal's web site is located at http://www.inderscience.com/ijbra
dc.publisher.placeUnited Kingdom
dc.relation.ispartofInternational Journal of Bioinformatics Research and Applications
dc.relation.referencesReferences in Scopus
dc.rightsInternational Journal of Bioinformatics Research and Applications. Copyright © Inderscience Publishers.
dc.subject.meshArtificial Intelligence
dc.subject.meshBayes Theorem
dc.subject.meshComputational Biology
dc.subject.meshDatabases, Genetic
dc.subject.meshEnzymes - genetics
dc.subject.meshMetabolic Networks and Pathways
dc.subject.meshRegression Analysis
dc.titleComparison of Bayesian and regression models in missing enzyme identification
dc.typeArticle
Author Affiliations
  1. The University of Hong Kong
  2. Methodist Hospital Houston