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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, B2 2 1
Zhou, X2 2
Hung, YS1
Wong, S2 2
 
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
2012 SCImago Journal Rankings: 0.189
 
DOIhttp://dx.doi.org/10.1504/IJBRA.2008.021174
 
ReferencesReferences in Scopus
 
DC FieldValue
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
2012 SCImago Journal Rankings: 0.189
 
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
 
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Author Affiliations
  1. The University of Hong Kong
  2. Methodist Hospital Houston