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dc.contributor.authorRezwan, F.
dc.contributor.authorSun, Yi.
dc.contributor.authorRobinson, M.
dc.contributor.authorAdams, R.G.
dc.contributor.authorRust, A.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2009-10-19T09:17:42Z
dc.date.available2009-10-19T09:17:42Z
dc.date.issued2008
dc.identifier.citationRezwan , F , Sun , Y , Robinson , M , Adams , R G , Rust , A G & Davey , N 2008 , ' Combining experts in order to identify binding sites in genomic data ' , Proceedings of the , vol. 2008 .
dc.identifier.otherPURE: 94345
dc.identifier.otherPURE UUID: 95c225a4-2512-408a-a0ac-6ebebd66e363
dc.identifier.otherdspace: 2299/3966
dc.identifier.urihttp://hdl.handle.net/2299/3966
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 classify the combination of the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms. The resulting classifier produces fewer false positive predictions and so reduces the expensive experimental procedure of verifying the predictions.en
dc.language.isoeng
dc.relation.ispartofProceedings of the
dc.titleCombining experts in order to identify binding sites in genomic dataen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionScience, Technology and Creative Arts Central
dc.description.statusPeer reviewed
dc.relation.school
dcterms.dateAccepted2008
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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