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dc.contributor.authorHsu, Justin Bo-Kaien_US
dc.contributor.authorBretana, Neil Arvinen_US
dc.contributor.authorLee, Tzong-Yien_US
dc.contributor.authorHuang, Hsien-Daen_US
dc.date.accessioned2014-12-08T15:21:01Z-
dc.date.available2014-12-08T15:21:01Z-
dc.date.issued2011-11-16en_US
dc.identifier.issn1932-6203en_US
dc.identifier.urihttp://dx.doi.org/10.1371/journal.pone.0027567en_US
dc.identifier.urihttp://hdl.handle.net/11536/14931-
dc.description.abstractRegulation of pre-mRNA splicing is achieved through the interaction of RNA sequence elements and a variety of RNA-splicing related proteins (splicing factors). The splicing machinery in humans is not yet fully elucidated, partly because splicing factors in humans have not been exhaustively identified. Furthermore, experimental methods for splicing factor identification are time-consuming and lab-intensive. Although many computational methods have been proposed for the identification of RNA-binding proteins, there exists no development that focuses on the identification of RNA-splicing related proteins so far. Therefore, we are motivated to design a method that focuses on the identification of human splicing factors using experimentally verified splicing factors. The investigation of amino acid composition reveals that there are remarkable differences between splicing factors and non-splicing proteins. A support vector machine (SVM) is utilized to construct a predictive model, and the five-fold cross-validation evaluation indicates that the SVM model trained with amino acid composition could provide a promising accuracy (80.22%). Another basic feature, amino acid dipeptide composition, is also examined to yield a similar predictive performance to amino acid composition. In addition, this work presents that the incorporation of evolutionary information and domain information could improve the predictive performance. The constructed models have been demonstrated to effectively classify (73.65% accuracy) an independent data set of human splicing factors. The result of independent testing indicates that in silico identification could be a feasible means of conducting preliminary analyses of splicing factors and significantly reducing the number of potential targets that require further in vivo or in vitro confirmation.en_US
dc.language.isoen_USen_US
dc.titleIncorporating Evolutionary Information and Functional Domains for Identifying RNA Splicing Factors in Humansen_US
dc.typeArticleen_US
dc.identifier.doi10.1371/journal.pone.0027567en_US
dc.identifier.journalPLOS ONEen_US
dc.citation.volume6en_US
dc.citation.issue11en_US
dc.citation.epageen_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:000297555400057-
dc.citation.woscount4-
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