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dc.contributor.authorSun, Yi
dc.contributor.authorRobinson, M.
dc.contributor.authorAdams, Roderick
dc.contributor.authorKaye, Paul H.
dc.contributor.authorRust, A.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2011-08-09T09:26:55Z
dc.date.available2011-08-09T09:26:55Z
dc.date.issued2005
dc.identifier.citationSun , Y , Robinson , M , Adams , R , Kaye , P H , Rust , A G & Davey , N 2005 , Using real-valued metaclassifiers to integrate binding site predictions . in In: Procs of IJCNN 2005, Int Joint Conference on Neural networks . vol. 1 , IEEE , pp. 481-486 .
dc.identifier.otherPURE: 303891
dc.identifier.otherPURE UUID: 8bbfa48e-3647-4db2-bd3e-e336a12536f6
dc.identifier.otherORCID: /0000-0001-6950-4870/work/32372023
dc.identifier.urihttp://hdl.handle.net/2299/6105
dc.description“This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder." “Copyright IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.” Original article can be found at: http://ieeexplore.ieee.org/xpl/RecentCon.jsp?punumber=10421
dc.description.abstractCurrently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these 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 and support vector machines on predictions from 12 key real valued algorithms. Furthermore, we use a ‘window’ of consecutive results in 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 outperform each of the original individual algorithms and the other classifiers employed in this work. In particular they have a better tradeoff between recall and precision.en
dc.language.isoeng
dc.publisherIEEE
dc.relation.ispartofIn: Procs of IJCNN 2005, Int Joint Conference on Neural networks
dc.rightsOpen
dc.titleUsing real-valued metaclassifiers to integrate binding site predictionsen
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
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.relation.schoolSchool of Computer Science
dc.relation.schoolSchool of Engineering and Computer Science
dc.description.versiontypeFinal Accepted Version
dcterms.dateAccepted2005
rioxxterms.versionAM
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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