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dc.contributor.authorSun, Yi.
dc.contributor.authorRobinson, M.
dc.contributor.authorAdams, R.G.
dc.contributor.authorTe Boekhorst, R.
dc.contributor.authorRust, A.G.
dc.contributor.authorDavey, N.
dc.date.accessioned2007-10-01T10:01:00Z
dc.date.available2007-10-01T10:01:00Z
dc.date.issued2006
dc.identifier.citationSun , Y , Robinson , M , Adams , R G , Te Boekhorst , R , Rust , A G & Davey , N 2006 , Using sampling methods to improve binding site predictions . in In: Procs of the 14th European Symposium on Artificial Neural Networks, ESANN 2006 . pp. 533-538 .
dc.identifier.isbn2-930307-06-4
dc.identifier.otherdspace: 2299/779
dc.identifier.urihttp://hdl.handle.net/2299/779
dc.description.abstractCurrently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. In previous work we combine random selection under-sampling into SMOTE over-sampling technique, working with several classification algorithms from machine learning field to integrate binding site predictions. In this paper, we improve the classification result with the aid of Tomek links as an either undersampling or cleaning technique.en
dc.format.extent168570
dc.language.isoeng
dc.relation.ispartofIn: Procs of the 14th European Symposium on Artificial Neural Networks, ESANN 2006
dc.titleUsing sampling methods to improve binding site predictionsen
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
rioxxterms.typeOther
herts.preservation.rarelyaccessedtrue


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