Title: Prediction of protein secondary structure using large margin nearest neighbour classification

Authors: Wei Yang; Kuanquan Wang; Wangmeng Zuo

Addresses: Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China; School of Computer and Information Engineering, HeNan University, Kaifeng, 475004, China ' Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China ' Biocomputing Research Centre, School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China

Abstract: In this paper, we introduce a novel method for protein secondary structure prediction by using Position-Specific Scoring Matrices (PSSM) profiles and Large Margin Nearest Neighbour (LMNN) classification. Since the PSSM profiles are not specifically designed for protein secondary structure prediction, the traditional nearest neighbour method could not achieve satisfactory prediction accuracy. To address this problem, we first use a LMNN model to learn a Mahalanobis distance metric for nearest neighbour classification. Then, an energy-based rule is invoked to assign secondary structure. Tests show that the proposed method obtains better prediction accuracy when compared with previous nearest neighbour methods.

Keywords: nearest neighbour; distance metric; large margin; protein secondary structure prediction; bioinformatics; PSSM profiles; LMNN classification.

DOI: 10.1504/IJBRA.2013.052445

International Journal of Bioinformatics Research and Applications, 2013 Vol.9 No.2, pp.207 - 219

Received: 24 Nov 2009
Accepted: 04 Mar 2011
Published online: 06 Sep 2014 *

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