Sequence-based Structural B-cell Epitope Prediction by Using Two Layer SVM Model and Association Rule Features

doi:10.2174/1574893614666181123155831

中山醫學大學機構典藏 CSMUIR > 中山醫學大學研究成果 > 期刊論文 > Item 310902500/24572

Please use this identifier to cite or link to this item: https://ir.csmu.edu.tw:8080/ir/handle/310902500/24572

Title:	Sequence-based Structural B-cell Epitope Prediction by Using Two Layer SVM Model and Association Rule Features
Authors:	Kuo, JH;Chang, CC;Chen, CW;Liang, HH;Chang, CY;Chu, YW
Keywords:	Structural epitope;support vector machines;association rule;position-specific scoring matrix;immune;pathogens
Date:	2020
Issue Date:	2022-08-09T08:04:33Z (UTC)
Publisher:	BENTHAM SCIENCE PUBL LTD
ISSN:	1574-8936
Abstract:	Background: Immune reaction is the most important defense mechanism for destroying invading pathogens in our body, and the epitope is the position of the antigen-antibody interaction on pathogenic proteins. Objective: The majority of epitopes are structural; however, the existing sequence-based predicting websites still have several methods to improve the predicting performance. Therefore, in this study, we used SVM as a machine learning tool to predict the epitope-based on protein sequences. Methods: Firstly, we built five SVM models in the first layer according to five features, including binary composition, position-specific scoring matrix, secondary structure, accessible surface area, and association rule, and then chose the patterns that exhibited the best performance in each model. Secondly, using the confidence score of the first-layer models as the input value for the SVM model in the second layer, that SVM model was integrated into the first-layer SVM models for improving the predicting accuracy. Results: The final prediction model was able to achieve up to 63% accuracy in predicting epitope results, and the predicting performance was better than that achieved by the existing predicting websites. Conclusion: Finally, a case study using a two-subunit cytochrome c oxidase of Paracoccus denitrificans was tested, achieving an accuracy of up to 66%.
URI:	http://dx.doi.org/10.2174/1574893614666181123155831 https://www.webofscience.com/wos/woscc/full-record/WOS:000536285200008 https://ir.csmu.edu.tw:8080/handle/310902500/24572
Relation:	CURRENT BIOINFORMATICS ,2020 ,v15 ,issue 3 ,p246-252
Appears in Collections:	[中山醫學大學研究成果] 期刊論文

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