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dc.contributor.authorHuang, Wen-Linen_US
dc.contributor.authorTsai, Ming-Juen_US
dc.contributor.authorHsu, Kai-Tien_US
dc.contributor.authorWang, Jyun-Rongen_US
dc.contributor.authorChen, Yi-Hsiungen_US
dc.contributor.authorHo, Shinn-Yingen_US
dc.date.accessioned2019-04-03T06:39:53Z-
dc.date.available2019-04-03T06:39:53Z-
dc.date.issued2015-01-01en_US
dc.identifier.issn1755-8794en_US
dc.identifier.urihttp://dx.doi.org/10.1186/1755-8794-8-S4-S3en_US
dc.identifier.urihttp://hdl.handle.net/11536/136167-
dc.description.abstractBackground: High genetic heterogeneity in the hepatitis C virus (HCV) is the major challenge of the development of an effective vaccine. Existing studies for developing HCV vaccines have mainly focused on T-cell immune response. However, identification of linear B-cell epitopes that can stimulate B-cell response is one of the major tasks of peptide-based vaccine development. Owing to the variability in B-cell epitope length, the prediction of B-cell epitopes is much more complex than that of T-cell epitopes. Furthermore, the motifs of linear B-cell epitopes in different pathogens are quite different (e.g. HCV and hepatitis B virus). To cope with this challenge, this work aims to propose an HCV-customized sequence-based prediction method to identify B-cell epitopes of HCV. Results: This work establishes an experimentally verified dataset comprising the B-cell response of HCV dataset consisting of 774 linear B-cell epitopes and 774 non B-cell epitopes from the Immune Epitope Database. An interpretable rule mining system of B-cell epitopes (IRMS-BE) is proposed to select informative physicochemical properties (PCPs) and then extracts several if-then rule-based knowledge for identifying B-cell epitopes. A web server Bcell-HCV was implemented using an SVM with the 34 informative PCPs, which achieved a training accuracy of 79.7% and test accuracy of 70.7% better than the SVM-based methods for identifying B-cell epitopes of HCV and the two general-purpose methods. This work performs advanced analysis of the 34 informative properties, and the results indicate that the most effective property is the alpha-helix structure of epitopes, which influences the connection between host cells and the E2 proteins of HCV. Furthermore, 12 interpretable rules are acquired from top-five PCPs and achieve a sensitivity of 75.6% and specificity of 71.3%. Finally, a conserved promising vaccine candidate, PDREMVLYQE, is identified for inclusion in a vaccine against HCV. Conclusions: This work proposes an interpretable rule mining system IRMS-BE for extracting interpretable rules using informative physicochemical properties and a web server Bcell-HCV for predicting linear B-cell epitopes of HCV. IRMS-BE may also apply to predict B-cell epitopes for other viruses, which benefits the improvement of vaccines development of these viruses without significant modification. Bcell-HCV is useful for identifying B-cell epitopes of HCV antigen to help vaccine development, which is available at http://e045.life.nctu.edu.tw/BcellHCV.en_US
dc.language.isoen_USen_US
dc.titlePrediction of linear B-cell epitopes of hepatitis C virus for vaccine developmenten_US
dc.typeArticleen_US
dc.identifier.doi10.1186/1755-8794-8-S4-S3en_US
dc.identifier.journalBMC MEDICAL GENOMICSen_US
dc.citation.volume8en_US
dc.citation.spage0en_US
dc.citation.epage0en_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:000382998600003en_US
dc.citation.woscount3en_US
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