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Applied Computational Intelligence and Soft Computing
Volume 2013 (2013), Article ID 794350, 9 pages
http://dx.doi.org/10.1155/2013/794350
Research Article

On the Variability of Neural Network Classification Measures in the Protein Secondary Structure Prediction Problem

Department of Computer Science, Morgan State University, Baltimore, MD 21251, USA

Received 27 April 2012; Revised 19 November 2012; Accepted 27 November 2012

Academic Editor: Cheng-Jian Lin

Copyright © 2013 Eric Sakk and Ayanna Alexander. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

We revisit the protein secondary structure prediction problem using linear and backpropagation neural network architectures commonly applied in the literature. In this context, neural network mappings are constructed between protein training set sequences and their assigned structure classes in order to analyze the class membership of test data and associated measures of significance. We present numerical results demonstrating that classifier performance measures can vary significantly depending upon the classifier architecture and the structure class encoding technique. Furthermore, an analytic formulation is introduced in order to substantiate the observed numerical data. Finally, we analyze and discuss the ability of the neural network to accurately model fundamental attributes of protein secondary structure.