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dc.contributor.authorSun, Yi
dc.contributor.authorCastellano, C.G.
dc.contributor.authorRobinson, M.
dc.contributor.authorAdams, R.G.
dc.contributor.authorRust, A.G.
dc.contributor.authorDavey, N.
dc.identifier.citationSun , Y , Castellano , C G , Robinson , M , Adams , R G , Rust , A G & Davey , N 2009 , ' Using pre and post-processing methods to improve binding site predictions ' , Pattern Recognition , vol. 42 , no. 9 , pp. 1949-1958 .
dc.identifier.otherdspace: 2299/3652
dc.descriptionOriginal article can be found at: Copyright Elsevier Ltd.
dc.description.abstractCurrently the best algorithms for transcription factor binding site prediction within sequences of regulatory DNA are severely limited in accuracy. In this paper, we integrate 12 original binding site prediction algorithms, and use a `window' of consecutive predictions in order to contextualise the neighbouring results. We combine either random selection or Tomek links under-sampling with SMOTE over-sampling techniques. In addition, we investigate the behaviour of four feature selection filtering methods: Bi-Normal Separation, Correlation Coefficients, F-Score and a cross entropy based algorithm. Finally, 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 algorithms.en
dc.relation.ispartofPattern Recognition
dc.subjectTomek link
dc.subjectSupport Vector Machines
dc.subjectTranscription Factors
dc.titleUsing pre and post-processing methods to improve binding site predictionsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
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 and Creative Arts Central
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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