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Please use this identifier to cite or link to this item: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44270

Title: A hybrid method of propensity scales and support vector machine in a linear epitope prediction
Authors: Hsin-Wei Wang
Ya-Chi Lin
Tun-Wen Pai
Pei-Wen Tsai
Contributors: 國立臺灣海洋大學:資訊工程學系
NTOU:Department of Computer Science and Engineering
Keywords: linear epitope
support vector machine
physicochemical property
antibody-antigen
amino acid segment
Date: 2011-06
Issue Date: 2017-11-21T06:30:18Z
Publisher: The 5th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS 2011)
Abstract: Abstract:An epitope activates B cells to amplify and induce antibodies which can neutralize the foreign molecules, particles and pathogens. It also plays a crucial role in developing synthetic peptides for vaccination. Identification of epitopes using biological screening approaches is time consuming and high cost. Therefore, bioinformatics approaches are developed to enhance the speed of identifying the epitopes and conserve time. Herein, a combinatorial methodology based on physico-chemical properties and SVM (Support Vector Machine) techniques was proposed to address the aim of this study. Datasets of epitope and non epitope segments with 2, 3 and 4 residues in length were trained and applied as statistical features of SVM. After training, three datasets including one curated and two public ones were employed to evaluate the performance of the proposed system which was also compared with four existing LE predictors, BepiPred, ABCpred, BCPred and FBCPred. Our proposed system has presented better specificity, accuracy, and positive prediction value (PPV) in most testing cases. High specificity and PPV of a linear epitope prediction can lead to an efficient and effective design on biological experiments.
URI: http://ntour.ntou.edu.tw:8080/ir/handle/987654321/44270
Appears in Collections:[資訊工程學系] 演講及研討會

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