Title: Protein structural classification using orthogonal transformation and class-association rules

Authors: Sumeet Dua, Praveen C. Kidambi

Addresses: Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, USA. ' Department of Computer Science, Louisiana Tech University, Ruston, LA 71272, USA

Abstract: Protein structure classification and comparison is a central area in the field of bioinformatics. Rapidly increasing protein structure databases commonly suffer from the |curse of dimensionality|, necessitating the development of the dimensionality reduction of structural information prior to its classification. We propose a novel automated algorithmic framework for three-dimensional structure-based classification of proteins using orthogonal transformation of the geometric shape descriptors derived from protein structures, and then employing an association rule-based supervised clustering approach. The proposed computational framework demonstrates, on two different data sets, the applicability of association rule discovery-based classification of structural descriptors for protein fold classification with improved sensitivity.

Keywords: proteins; protein structure; protein classification; orthogonal transformation; association rules; dimensionality reduction; dihedral angles; rule classification; Laplace accuracy; geometric descriptors; cosine transformation; bioinformatics; supervised clustering.

DOI: 10.1504/IJDMB.2010.032149

International Journal of Data Mining and Bioinformatics, 2010 Vol.4 No.2, pp.175 - 190

Received: 29 May 2008
Accepted: 14 Oct 2008
Published online: 11 Mar 2010 *

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