Title: Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features

Authors: Wen Zhang; Juan Liu; Meng Zhao; Qingjiao Li

Addresses: School of Computer Science, Wuhan University, Wuhan 430072, China ' School of Computer Science, Wuhan University, Wuhan 430072, China ' School of Computer Science, Wuhan University, Wuhan 430072, China ' Shool of Computer Science, Wuhan University, Wuhan 430072, China

Abstract: The identification of linear B-cell epitopes is important for developing epitope-based vaccines. Recently, machine learning techniques have been used in the epitope prediction, but the existing encoding schemes usually neglected valuable discriminative information. In this paper, we proposed a novel encoding scheme which combines several groups of sequence-derived structural and physicochemical features, and support vector machine was used to construct the prediction models. When applied to the benchmark dataset, our proposed method demonstrated better results than benchmark methods. Moreover, the study indicated incorporating more discriminative features may contribute to the higher prediction performance.

Keywords: linear B-cell epitopes; peptide sequences; structural features; physicochemical features; SVM; support vector machine; feature selection; vaccines; vaccine development; machine learning; prediction models; bioinformatics; encoding schemes.

DOI: 10.1504/IJDMB.2012.049298

International Journal of Data Mining and Bioinformatics, 2012 Vol.6 No.5, pp.557 - 569

Received: 26 Aug 2010
Accepted: 01 Jan 2011
Published online: 17 Dec 2014 *

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