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dc.contributor.authorYang, ZR
dc.date.accessioned2013-06-06T10:34:33Z
dc.date.issued2009-05
dc.description.abstractCollagen hydroxyproline is an important posttranslational modification activity because of its close relationship with various diseases and signaling activities. However, there is no study to date for constructing models for predicting collagen hydroxyproline sites. Support vector machines with two kernel functions (the identity kernel function and the bio-kernel function) have been used for constructing models for predicting collagen hydroxyproline sites in this study. The models are constructed based on 37 sequences collected from NCBI. Peptide data are generated using a sliding window with various sizes to scan the sequences. Fivefold cross-validation is used for model evaluation. The best model has specificity of 70% and sensitivity of 90%.en_GB
dc.identifier.citationJournal of Computational Biology, 2009, Vol. 16, Issue 5, pp. 691 - 702en_GB
dc.identifier.doi10.1089/cmb.2008.0167
dc.identifier.urihttp://hdl.handle.net/10871/9890
dc.language.isoenen_GB
dc.publisherMary Ann Lieberten_GB
dc.relation.urlhttp://www.ncbi.nlm.nih.gov/pubmed/19432539en_GB
dc.relation.urlhttp://online.liebertpub.com/doi/abs/10.1089/cmb.2008.0167en_GB
dc.subjectAlgorithmsen_GB
dc.subjectAmino Acid Sequenceen_GB
dc.subjectArtificial Intelligenceen_GB
dc.subjectCollagenen_GB
dc.subjectHydroxyprolineen_GB
dc.subjectMathematicsen_GB
dc.subjectModels, Chemicalen_GB
dc.subjectMolecular Sequence Dataen_GB
dc.subjectPeptidesen_GB
dc.subjectROC Curveen_GB
dc.subjectReproducibility of Resultsen_GB
dc.subjectSensitivity and Specificityen_GB
dc.subjectSequence Analysis, Proteinen_GB
dc.titlePredict collagen hydroxyproline sites using support vector machines.en_GB
dc.typeArticleen_GB
dc.date.available2013-06-06T10:34:33Z
exeter.place-of-publicationUnited States
dc.descriptionaddresses: School of Biosciences, University of Exeter, Exeter, United Kingdom. z.r.yang@ex.ac.uken_GB
dc.descriptiontypes: Journal Articleen_GB
dc.descriptionThis is a copy of an article published in the Journal of Computational Biology © 2009 copyright Mary Ann Liebert, Inc.; Journal of Computational Biology is available online at: http://online.liebertpub.com.en_GB
dc.identifier.journalJournal of Computational Biologyen_GB


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