Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/6254
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Electronic and Information Engineering | - |
dc.creator | Mak, MW | - |
dc.creator | Wang, W | - |
dc.creator | Kung, SY | - |
dc.date.accessioned | 2014-12-11T08:22:42Z | - |
dc.date.available | 2014-12-11T08:22:42Z | - |
dc.identifier.uri | http://hdl.handle.net/10397/6254 | - |
dc.language.iso | en | en_US |
dc.publisher | BioMed Central | en_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.title | Fast subcellular localization by cascaded fusion of signal-based and homology-based methods | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.description.otherinformation | Author name used in this publication: Wei Wang | en_US |
dc.identifier.volume | 9 | - |
dc.identifier.issue | Suppl 1 | - |
dc.identifier.doi | 10.1186/1477-5956-9-S1-S8 | - |
dcterms.abstract | Background: 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.abstract | Results: 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.abstract | Conclusions: 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.accessRights | open access | en_US |
dcterms.bibliographicCitation | Proteome science, 14 Oct. 2011, v. 9, suppl. 1, S8, p.1-12 | - |
dcterms.isPartOf | Proteome science | - |
dcterms.issued | 2011-10-14 | - |
dc.identifier.isi | WOS:000299782200008 | - |
dc.identifier.scopus | 2-s2.0-80054016849 | - |
dc.identifier.pmid | 22166017 | - |
dc.identifier.eissn | 1477-5956 | - |
dc.description.oa | Version of Record | en_US |
dc.identifier.FolderNumber | OA_IR/PIRA | en_US |
dc.description.pubStatus | Published | en_US |
dc.description.oaCategory | CC | en_US |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
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Mak_Subcellular_Localization_Signal-based.pdf | 1.04 MB | Adobe PDF | View/Open |
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