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dc.contributor.authorYu, CSen_US
dc.contributor.authorLin, CJen_US
dc.contributor.authorHwang, JKen_US
dc.date.accessioned2014-12-08T15:39:17Z-
dc.date.available2014-12-08T15:39:17Z-
dc.date.issued2004-05-01en_US
dc.identifier.issn0961-8368en_US
dc.identifier.urihttp://dx.doi.org/10.1110/ps.03479604en_US
dc.identifier.urihttp://hdl.handle.net/11536/26836-
dc.description.abstractGram-negative bacteria have five major subcellular localization sites: the cytoplasm, the periplasm, the inner membrane, the outer membrane, and the extracellular space. The subcellular location of a protein can provide valuable information about its function. With the rapid increase of sequenced genomic data, the need for an automated and accurate to predict subcellular localization becomes increasingly important. We present an approach to predict subcellular localization for Gram-negative bacteria. This method uses the support vector machines trained by multiple feature vectors based on n-peptide compositions. For a standard data set comprising 1443 proteins, the overall prediction accuracy reaches 89%, which, to the best of our knowledge, is the highest prediction rate ever reported. Our prediction is 14% higher than that of the recently developed multimodular PSORT-B. Because of its simplicity, this approach can be easily extended to other organisms and should be a useful tool for the high-throughput and large-scale analysis of proteomic and genomic data.en_US
dc.language.isoen_USen_US
dc.subjectsubcellular localizationen_US
dc.subjectsupport vector machineen_US
dc.subjectGram-negative bacteriaen_US
dc.subjectmachine-learning methoden_US
dc.subjectproteomeen_US
dc.subjectgenomeen_US
dc.subjectn-peptide compositionsen_US
dc.titlePredicting subcellular localization of proteins for Gram-negative bacteria by support vector machines based on n-peptide compositionsen_US
dc.typeArticleen_US
dc.identifier.doi10.1110/ps.03479604en_US
dc.identifier.journalPROTEIN SCIENCEen_US
dc.citation.volume13en_US
dc.citation.issue5en_US
dc.citation.spage1402en_US
dc.citation.epage1406en_US
dc.contributor.department生物科技學系zh_TW
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentDepartment of Biological Science and Technologyen_US
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000221042200024-
dc.citation.woscount187-
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