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dc.contributor.authorSun, Yi.
dc.contributor.authorRobinson, M.
dc.contributor.authorAdams, R.G.
dc.contributor.authorRust, A.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2008-10-01T12:02:23Z
dc.date.available2008-10-01T12:02:23Z
dc.date.issued2008
dc.identifier.citationSun , Y , Robinson , M , Adams , R G , Rust , A G & Davey , N 2008 , ' Prediction of Binding Sites in the Mouse Genome Using Support Vector Machines ' , Lecture Notes in Computer Science , pp. 91-100 .
dc.identifier.issn0302-9743
dc.identifier.otherPURE: 84106
dc.identifier.otherPURE UUID: 60ca66eb-0e9c-4dde-982e-9744f426baf1
dc.identifier.otherdspace: 2299/2411
dc.identifier.otherScopus: 51849097661
dc.identifier.urihttp://hdl.handle.net/2299/2411
dc.descriptionOriginal article can be found at http://springerlink.com Copyright Springer
dc.description.abstractComputational prediction of cis-regulatory binding sites is widely acknowledged as a difficult task. There are many different algorithms for searching for binding sites in current use. However, most of them produce a high rate of false positive predictions. Moreover, many algorithmic approaches are inherently constrained with respect to the range of binding sites that they can be expected to reliably predict. We propose to use SVMs to predict binding sites from multiple sources of evidence. We combine random selection under-sampling and the synthetic minority over-sampling technique to deal with the imbalanced nature of the data. In addition, we remove some of the final predicted binding sites on the basis of their biological plausibility. The results show that we can generate a new prediction that significantly improves on the performance of any one of the individual prediction algorithms.en
dc.language.isoeng
dc.relation.ispartofLecture Notes in Computer Science
dc.titlePrediction of Binding Sites in the Mouse Genome Using Support Vector Machinesen
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
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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