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dc.contributor.authorChang, Jia-Mingen_US
dc.contributor.authorSu, Emily Chia-Yuen_US
dc.contributor.authorLo, Allanen_US
dc.contributor.authorChiu, Hua-Shengen_US
dc.contributor.authorSung, Ting-Yien_US
dc.contributor.authorHsu, Wen-Lianen_US
dc.date.accessioned2014-12-08T15:11:04Z-
dc.date.available2014-12-08T15:11:04Z-
dc.date.issued2008-08-01en_US
dc.identifier.issn0887-3585en_US
dc.identifier.urihttp://dx.doi.org/10.1002/prot.21944en_US
dc.identifier.urihttp://hdl.handle.net/11536/8489-
dc.description.abstractPrediction of protein subcellular localization (PSL) is important for genome annotation, protein function prediction, and drug discovery. Many computational approaches for PSL prediction based on protein sequences have been proposed in recent years for Gram-negative bacteria. We present PSLDoc, a method based on gapped-dipeptides and probabilistic latent semantic analysis (PLSA) to solve this problem. A protein is considered as a term string composed by gapped-dipeptides, which are defined as any two residues separated by one or more positions. The weighting scheme of gapped-dipeptides is calculated according to a position specific score matrix, which includes sequence evolutionary information. Then, PLSA is applied for feature reduction, and reduced vectors are input to five one-versus-rest support vector machine classifiers. The localization site with the highest probability is assigned as the final prediction. It has been reported that there is a strong correlation between sequence homology and subcellular localization (Nair and Rost, Protein Sci 2002;11:2836-2847, Yu et al., Proteins 2006;64:643-651). To properly evaluate the performance of PSLDoc, a target protein can be classified into low- or high-homology data sets. PSLDoc's overall accuracy of low- and high-homology data sets reaches 86.84% and 98.219% respectively, and it compares favorably with that of CELLO H (Yu et al., Proteins 2006,64:643-651). In addition, we set a confidence threshold to achieve a high precision at specified levels of recall rates. When the confidence threshold is set at 0.7, PSLDoc achieves 97.89% in precision which is considerably better than that of PSORTb v.2.0 (Gardy et al., Bioinformatics 2005,21:617-623). Our approach demonstrates that the specific feature representation for proteins can be successfully applied to the prediction of protein subcellular localization and improves prediction accuracy. Besides, because of the generality of the representation, our method can be extended to eukaryotic proteomes in the future. The web server of PSLDoc is publicly available at http://bio-cluster.iis.sinica.edu.tw/similar to bioapp/PSLDoc/.en_US
dc.language.isoen_USen_US
dc.subjectprotein subcellular localizationen_US
dc.subjectdocument classificationen_US
dc.subjectvector space modelen_US
dc.subjectgapped-dipeptidesen_US
dc.subjectprobabilistic latent semantic analysisen_US
dc.subjectsupport vector machinesen_US
dc.titlePSLOoc: Protein subcellular localization prediction based on gapped-dipeptides and probabilistic latent semantic analysisen_US
dc.typeArticleen_US
dc.identifier.doi10.1002/prot.21944en_US
dc.identifier.journalPROTEINS-STRUCTURE FUNCTION AND BIOINFORMATICSen_US
dc.citation.volume72en_US
dc.citation.issue2en_US
dc.citation.spage693en_US
dc.citation.epage710en_US
dc.contributor.department生物資訊及系統生物研究所zh_TW
dc.contributor.departmentInstitude of Bioinformatics and Systems Biologyen_US
dc.identifier.wosnumberWOS:000257156500014-
dc.citation.woscount22-
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