Predicting linear B-cell epitopes by using sequence-derived structural and physicochemical features. (1st January 2012)

• Record Type: Journal Article
• Authors: Zhang, Wen; Liu, Juan; Zhao, Meng; Li, Qingjiao
• Is Part Of: International journal of data mining and bioinformatics. Volume 6:Number 5(2012)
• Page Start: 557
• Page End: 569
• Publication Date: 2012-01-01
• Subjects: linear B-cell epitopes -- sequence-derived structural and physicochemical features -- SVM -- support vector machine -- feature selection; Data mining -- Periodicals; Bioinformatics -- Periodicals; 006.312
• Languages: English
• ISSNs: 1748-5673

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.