Title: SVR-PAIRWISE method to predict MHC-II binding peptides

Authors: Juan Liu, Lian Wang, Shanfeng Zhu

Addresses: School of Computer, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. ' School of Computer, Wuhan University, 129 Luoyu Road, Wuhan 430079, China. ' Shanghai Key Laboratory of Intelligent Information Processing, School of Computer Science, Fudan University, 220 Handan Road, Shanghai 200433, China

Abstract: Peptides binding to MHC molecules is of great importance in the process of triggering and initiating immune responses. There are two main kinds of MHC molecules, MHC class I and class II, and the prediction of MHC-II binding peptides is much more difficult due to their variable lengths, which makes it difficult to construct a preferable prediction model by using most of the existing methods. This paper presents a method, called as SVR-PAIRWISE, to combine Support Vector Regression (SVR) and pairwise alignment, to quantitatively predict the MHC-II binding peptides. The comparison results with some popular methods show its satisfying performances.

Keywords: SVR; support vector regression; major histocompatibility complex class II; binding peptides; pairwise alignment; bioinformatics; MHC molecules; immune responses.

DOI: 10.1504/IJBRA.2010.031292

International Journal of Bioinformatics Research and Applications, 2010 Vol.6 No.1, pp.58 - 68

Published online: 27 Jan 2010 *

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