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dc.contributor.authorRezwan, Faisal
dc.contributor.authorSun, Yi
dc.contributor.authorDavey, N.
dc.contributor.authorAdams, Roderick
dc.contributor.authorRust, A.G.
dc.contributor.authorRobinson, M.
dc.identifier.citationRezwan , F , Sun , Y , Davey , N , Adams , R , Rust , A G & Robinson , M 2011 , ' Effect of using varying negative examples in transcription factor binding site predictions ' , Lecture Notes in Computer Science , vol. 6623 , pp. 1-12 .
dc.identifier.otherPURE: 333459
dc.identifier.otherPURE UUID: aacacc60-f2be-4193-98b5-59ee6d98ceeb
dc.identifier.otherScopus: 79955767455
dc.descriptionThe original publication is available at Copyright Springer
dc.description.abstractIdentifying transcription factor binding sites computationally is a hard problem as it produces many false predictions. Combining the predictions from existing predictors can improve the overall predictions by using classification methods like Support Vector Machines (SVM). But conventional negative examples (that is, example of non-binding sites) in this type of problem are highly unreliable. In this study, we have used different types of negative examples. One class of the negative examples has been taken from far away from the promoter regions, where the occurrence of binding sites is very low, and another one has been produced by randomization. Thus we observed the effect of using different negative examples in predicting transcription factor binding sites in mouse. We have also devised a novel cross-validation technique for this type of biological problem.en
dc.relation.ispartofLecture Notes in Computer Science
dc.titleEffect of using varying negative examples in transcription factor binding site predictionsen
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionSchool of Computer Science
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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