Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/6254
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dc.contributorDepartment of Electronic and Information Engineering-
dc.creatorMak, MW-
dc.creatorWang, W-
dc.creatorKung, SY-
dc.date.accessioned2014-12-11T08:22:42Z-
dc.date.available2014-12-11T08:22:42Z-
dc.identifier.urihttp://hdl.handle.net/10397/6254-
dc.language.isoenen_US
dc.publisherBioMed Centralen_US
dc.rights© 2011 Mak et al; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.en_US
dc.titleFast subcellular localization by cascaded fusion of signal-based and homology-based methodsen_US
dc.typeJournal/Magazine Articleen_US
dc.description.otherinformationAuthor name used in this publication: Wei Wangen_US
dc.identifier.volume9-
dc.identifier.issueSuppl 1-
dc.identifier.doi10.1186/1477-5956-9-S1-S8-
dcterms.abstractBackground: The functions of proteins are closely related to their subcellular locations. In the post-genomics era, the amount of gene and protein data grows exponentially, which necessitates the prediction of subcellular localization by computational means.-
dcterms.abstractResults: This paper proposes mitigating the computation burden of alignment-based approaches to subcellular localization prediction by a cascaded fusion of cleavage site prediction and profile alignment. Specifically, the informative segments of protein sequences are identified by a cleavage site predictor using the information in their N-terminal shorting signals. Then, the sequences are truncated at the cleavage site positions, and the shortened sequences are passed to PSI-BLAST for computing their profiles. Subcellular localization are subsequently predicted by a profile-to-profile alignment support-vector-machine (SVM) classifier. To further reduce the training and recognition time of the classifier, the SVM classifier is replaced by a new kernel method based on the perturbational discriminant analysis (PDA).-
dcterms.abstractConclusions: Experimental results on a new dataset based on Swiss-Prot Release 57.5 show that the method can make use of the best property of signal- and homology-based approaches and can attain an accuracy comparable to that achieved by using full-length sequences. Analysis of profile-alignment score matrices suggest that both profile creation time and profile alignment time can be reduced without significant reduction in subcellular localization accuracy. It was found that PDA enjoys a short training time as compared to the conventional SVM. We advocate that the method will be important for biologists to conduct large-scale protein annotation or for bioinformaticians to perform preliminary investigations on new algorithms that involve pairwise alignments.-
dcterms.accessRightsopen accessen_US
dcterms.bibliographicCitationProteome science, 14 Oct. 2011, v. 9, suppl. 1, S8, p.1-12-
dcterms.isPartOfProteome science-
dcterms.issued2011-10-14-
dc.identifier.isiWOS:000299782200008-
dc.identifier.scopus2-s2.0-80054016849-
dc.identifier.pmid22166017-
dc.identifier.eissn1477-5956-
dc.description.oaVersion of Recorden_US
dc.identifier.FolderNumberOA_IR/PIRAen_US
dc.description.pubStatusPublisheden_US
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