dc.contributor.author | Robinson, M. | |
dc.contributor.author | Gonzalez Castellano, C. | |
dc.contributor.author | Rezwan, F. | |
dc.contributor.author | Adams, R.G. | |
dc.contributor.author | Davey, N. | |
dc.contributor.author | Rust, A.G. | |
dc.contributor.author | Sun, Yi. | |
dc.date.accessioned | 2013-01-14T11:28:58Z | |
dc.date.available | 2013-01-14T11:28:58Z | |
dc.date.issued | 2008 | |
dc.identifier.citation | Robinson , M , Gonzalez Castellano , C , Rezwan , F , Adams , R G , Davey , N , Rust , A G & Sun , Y 2008 , ' Combining experts in order to identify binding sites in yeast and mouse genomic data ' , Neural Networks , vol. 21 , no. 6 , pp. 856-861 . https://doi.org/10.1016/j.neunet.2008.07.004 | |
dc.identifier.issn | 0893-6080 | |
dc.identifier.other | PURE: 94476 | |
dc.identifier.other | PURE UUID: 1fa2f321-bac5-481e-be20-246772c247d7 | |
dc.identifier.other | dspace: 2299/3960 | |
dc.identifier.other | Scopus: 50449092129 | |
dc.identifier.uri | http://hdl.handle.net/2299/9603 | |
dc.description | Original article can be found at: http://www.sciencedirect.com/science/journal/08936080 Copyright Elsevier Ltd. DOI: 10.1016/j.neunet.2008.07.004 | |
dc.description.abstract | The identification of cis-regulatory binding sites in DNA is a difficult problem in computational biology. To obtain a full understanding of the complex machinery embodied in genetic regulatory networks it is necessary to know both the identity of the regulatory transcription factors together with the location of their binding sites in the genome. We show that using an SVM together with data sampling, to integrate the results of individual algorithms specialised for the prediction of binding site locations, can produce significant improvements upon the original algorithms. These results make more tractable the expensive experimental procedure of actually verifying the predictions. | en |
dc.language.iso | eng | |
dc.relation.ispartof | Neural Networks | |
dc.subject | Transcription Factor Binding Sites | |
dc.subject | Support Vector Machine | |
dc.title | Combining experts in order to identify binding sites in yeast and mouse genomic data | en |
dc.contributor.institution | School of Computer Science | |
dc.contributor.institution | Science & Technology Research Institute | |
dc.contributor.institution | Science, Technology and Creative Arts Central | |
dc.contributor.institution | Centre for Computer Science and Informatics Research | |
dc.contributor.institution | Department of Computer Science | |
dc.contributor.institution | School of Physics, Engineering & Computer Science | |
dc.description.status | Peer reviewed | |
rioxxterms.versionofrecord | https://doi.org/10.1016/j.neunet.2008.07.004 | |
rioxxterms.type | Journal Article/Review | |
herts.preservation.rarelyaccessed | true | |