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dc.contributor.authorSun, Yi.
dc.contributor.authorRobinson, M.
dc.contributor.authorAdams, R.G.
dc.contributor.authorKaye, Paul H.
dc.contributor.authorTe Boekhorst, R.
dc.contributor.authorRust, A.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2012-11-14T12:00:06Z
dc.date.available2012-11-14T12:00:06Z
dc.date.issued2009
dc.identifier.citationSun , Y , Robinson , M , Adams , R G , Kaye , P H , Te Boekhorst , R , Rust , A G & Davey , N 2009 , ' Integrating genomic binding site predictions using real-valued meta classifiers ' , Neural Computing and Applications , vol. 18 , no. 6 , pp. 577-590 . https://doi.org/10.1007/s00521-008-0204-4
dc.identifier.issn0941-0643
dc.identifier.otherPURE: 160579
dc.identifier.otherPURE UUID: df7d8787-9be7-4288-a4df-a113c96810ba
dc.identifier.otherdspace: 2299/3917
dc.identifier.otherScopus: 70349495656
dc.identifier.otherORCID: /0000-0001-6950-4870/work/32372007
dc.identifier.urihttp://hdl.handle.net/2299/9158
dc.description“The original publication is available at www.springerlink.com”. Copyright Springer. DOI: 10.1007/s00521-008-0204-4
dc.description.abstractCurrently the best algorithms for predicting transcription factor binding sites in DNA sequences are severely limited in accuracy. There is good reason to believe that predictions from different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets, support vector machines and the Adaboost algorithm to predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results as the input vector in order to contextualise the neighbouring results. We improve the classification result with the aid of under- and over-sampling techniques. We find that support vector machines and the Adaboost algorithm outperform the original individual algorithms and the other classifiers employed in this work. In particular they give a better tradeoff between recall and precision.en
dc.language.isoeng
dc.relation.ispartofNeural Computing and Applications
dc.rightsOpen
dc.titleIntegrating genomic binding site predictions using real-valued meta classifiersen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Atmospheric and Climate Physics Research
dc.contributor.institutionParticle Instrumentation
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Engineering and Computer Science
dc.contributor.institutionScience, Technology and Creative Arts Central
dc.description.statusPeer reviewed
dc.relation.schoolSchool of Computer Science
dc.relation.schoolSchool of Engineering and Computer Science
dcterms.dateAccepted2009
rioxxterms.versionofrecordhttps://doi.org/10.1007/s00521-008-0204-4
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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