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dc.contributor.authorRobinson, M.
dc.contributor.authorGonzalez Castellano, C.
dc.contributor.authorRezwan, F.
dc.contributor.authorAdams, R.G.
dc.contributor.authorDavey, N.
dc.contributor.authorRust, A.G.
dc.contributor.authorSun, Yi.
dc.date.accessioned2013-01-14T11:28:58Z
dc.date.available2013-01-14T11:28:58Z
dc.date.issued2008
dc.identifier.citationRobinson , M , Gonzalez Castellano , C , Rezwan , F , Adams , R G , Davey , N , Rust , A G & Sun , Y 2008 , ' Combining experts in order to identify binding sites in yeast and mouse genomic data ' , Neural Networks , vol. 21 , no. 6 , pp. 856-861 . https://doi.org/10.1016/j.neunet.2008.07.004
dc.identifier.issn0893-6080
dc.identifier.otherPURE: 94476
dc.identifier.otherPURE UUID: 1fa2f321-bac5-481e-be20-246772c247d7
dc.identifier.otherdspace: 2299/3960
dc.identifier.otherScopus: 50449092129
dc.identifier.urihttp://hdl.handle.net/2299/9603
dc.descriptionOriginal article can be found at: http://www.sciencedirect.com/science/journal/08936080 Copyright Elsevier Ltd. DOI: 10.1016/j.neunet.2008.07.004
dc.description.abstractThe identification of cis-regulatory binding sites in DNA is a difficult problem in computational biology. To obtain a full understanding of the complex machinery embodied in genetic regulatory networks it is necessary to know both the identity of the regulatory transcription factors together with the location of their binding sites in the genome. We show that using an SVM together with data sampling, to integrate the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms. These results make more tractable the expensive experimental procedure of actually verifying the predictions.en
dc.language.isoeng
dc.relation.ispartofNeural Networks
dc.rightsOpen
dc.subjectTranscription Factor Binding Sites
dc.subjectSupport Vector Machine
dc.titleCombining experts in order to identify binding sites in yeast and mouse genomic dataen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionScience, Technology and Creative Arts Central
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Engineering and Computer Science
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dc.relation.schoolSchool of Engineering and Computer Science
dcterms.dateAccepted2008
rioxxterms.versionofrecordhttps://doi.org/10.1016/j.neunet.2008.07.004
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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