Protein fold recognition with adaptive local hyperplane algorithm

Authors:
Vojislav Kecman;Tao Yang
Affiliations:
Department of Computer Science, School of Engineering, Virginia Commonwealth University, Richmond, VA;Faculty of Engineering, The University of Auckland, Auckland, New Zealand
Venue:
CIBCB'09 Proceedings of the 6th Annual IEEE conference on Computational Intelligence in Bioinformatics and Computational Biology
Year:
2009

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Abstract

Protein fold recognition task is important for understanding the biological functions of proteins. The adaptive local hyperplane (ALH) algorithm has been shown to perform better than many other renown classifiers including support vector machines, K-nearest neighbor, linear discriminant analysis, K-local hyperplane distance nearest neighbor algorithms and decision trees on a variety of data sets. In this paper, we apply the ALH algorithm to well-known data sets on protein fold recognition task without sequence similarity from Ding and Dubchak (2001). The results obtained demonstrate that the ALH algorithm outperforms all the seven other very well known and established benchmarking classifiers applied to same data sets.