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dc.contributor.authorYu, Chin-Shengen_US
dc.contributor.authorChen, Yu-Chingen_US
dc.contributor.authorLu, Chih-Haoen_US
dc.contributor.authorHwang, Jenn-Kangen_US
dc.date.accessioned2014-12-08T15:16:01Z-
dc.date.available2014-12-08T15:16:01Z-
dc.date.issued2006-08-15en_US
dc.identifier.issn0887-3585en_US
dc.identifier.urihttp://dx.doi.org/10.1002/prot.21018en_US
dc.identifier.urihttp://hdl.handle.net/11536/11914-
dc.description.abstractBecause the protein's function is usually related to its subcellular localization, the ability to predict subcellular localization directly from protein sequences will be useful for inferring protein functions. Recent years have seen a surging interest in the development of novel computational tools to predict subcellular localization. At present, these approaches, based on a wide range of algorithms, have achieved varying degrees of success for specific organisms and for certain localization categories. A number of authors have noticed that sequence similarity is useful in predicting subcellular localization. For example, Nair and Rost (Protein Sci 2002;11:2836-2847) have carried out extensive analysis of the relation between sequence similarity and identity in subcellular localization, and have found a close relationship between them above a certain similarity threshold. However, many existing benchmark data sets used for the prediction accuracy assessment contain highly homologous sequences-some data sets comprising sequences up to 80-90% sequence identity. Using these benchmark test data will surely lead to overestimation of the performance of the methods considered. Here, we develop an approach based on a two-level support vector machine (SVM) system: the first level comprises a number of SVM classifiers, each based on a specific type of feature vectors derived from sequences; the second level SVM classifier functions as the jury machine to generate the probability distribution of decisions for possible localizations. We compare our approach with a global sequence alignment approach and other existing approaches for two benchmark data sets-one comprising prokaryotic sequences and the other eukaryotic sequences. Furthermore, we carried out all-against-all sequence alignment for several data sets to investigate the relationship between sequence homology and subcellular localization. Our results, which are consistent with previous studies, indicate that the homology search approach performs well down to 30% sequence identity, although its performance deteriorates considerably for sequences sharing lower sequence identity. A data set of high homology levels will undoubtedly lead to biased assessment of the performances of the predictive approaches-especially those relying on homology search or sequence annotations. Our two-level classification system based on SVM does not rely on homology search; therefore, its performance remains relatively unaffected by sequence homology. When compared with other approaches, our approach performed significantly better. Furthermore, we also develop a practical hybrid method, which combines the two-level SVM classifier and the homology search method, as a general tool for the sequence annotation of subcellular localization.en_US
dc.language.isoen_USen_US
dc.subjectsupport vector machinesen_US
dc.subjectsubcellular localizationen_US
dc.subjectsequence alignmenten_US
dc.titlePrediction of protein subcellular localizationen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/prot.21018en_US
dc.identifier.journalPROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICSen_US
dc.citation.volume64en_US
dc.citation.issue3en_US
dc.citation.spage643en_US
dc.citation.epage651en_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:000239103800007-
dc.citation.woscount214-
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