GOPred: GO molecular function prediction by combined classifiers

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dc.citation.issueNumber8en_US
dc.citation.volumeNumber5en_US
dc.contributor.authorSaraç Ö.S.en_US
dc.contributor.authorAtalay V.en_US
dc.contributor.authorCetin-Atalay, R.en_US
dc.date.accessioned2016-02-08T09:56:25Z
dc.date.available2016-02-08T09:56:25Z
dc.date.issued2010en_US
dc.departmentDepartment of Molecular Biology and Geneticsen_US
dc.description.abstractFunctional protein annotation is an important matter for in vivo and in silico biology. Several computational methods have been proposed that make use of a wide range of features such as motifs, domains, homology, structure and physicochemical properties. There is no single method that performs best in all functional classification problems because information obtained using any of these features depends on the function to be assigned to the protein. In this study, we portray a novel approach that combines different methods to better represent protein function. First, we formulated the function annotation problem as a classification problem defined on 300 different Gene Ontology (GO) terms from molecular function aspect. We presented a method to form positive and negative training examples while taking into account the directed acyclic graph (DAG) structure and evidence codes of GO. We applied three different methods and their combinations. Results show that combining different methods improves prediction accuracy in most cases. The proposed method, GOPred, is available as an online computational annotation tool (http://kinaz.fen.bilkent.edu.tr/gopred). © 2010 Saraç et al.en_US
dc.description.provenanceMade available in DSpace on 2016-02-08T09:56:25Z (GMT). No. of bitstreams: 1 bilkent-research-paper.pdf: 70227 bytes, checksum: 26e812c6f5156f83f0e77b261a471b5a (MD5) Previous issue date: 2010en
dc.identifier.doi10.1371/journal.pone.0012382en_US
dc.identifier.issn19326203
dc.identifier.urihttp://hdl.handle.net/11693/22168
dc.language.isoEnglishen_US
dc.relation.isversionofhttp://dx.doi.org/10.1371/journal.pone.0012382en_US
dc.source.titlePLoS ONEen_US
dc.subjectgene producten_US
dc.subjectprotein COBRA1en_US
dc.subjectprotein DDX11L1en_US
dc.subjectprotein FINP2en_US
dc.subjectprotein gcken_US
dc.subjectprotein GLRXen_US
dc.subjectprotein HELTen_US
dc.subjectprotein hrasen_US
dc.subjectprotein KIF18Ben_US
dc.subjectprotein KILLINen_US
dc.subjectprotein p53en_US
dc.subjectprotein PGAP1en_US
dc.subjectprotein RGL4en_US
dc.subjectunclassified drugen_US
dc.subjectproteinen_US
dc.subjectaccuracyen_US
dc.subjectarticleen_US
dc.subjectcontrolled studyen_US
dc.subjectdirected molecular evolutionen_US
dc.subjectgene structureen_US
dc.subjecthumanen_US
dc.subjectmathematical analysisen_US
dc.subjectmolecular biologyen_US
dc.subjectmolecular weighten_US
dc.subjectpredictionen_US
dc.subjectprotein functionen_US
dc.subjectreceiver operating characteristicen_US
dc.subjectsensitivity and specificityen_US
dc.subjectbiologyen_US
dc.subjectclassificationen_US
dc.subjectInterneten_US
dc.subjectmetabolismen_US
dc.subjectmethodologyen_US
dc.subjectComputational Biologyen_US
dc.subjectHumansen_US
dc.subjectInterneten_US
dc.subjectProteinsen_US
dc.titleGOPred: GO molecular function prediction by combined classifiersen_US
dc.typeArticleen_US

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