A neural network based approach for protein structural class prediction

Issue title: Theoretical advances of intelligent paradigms

Article type: Research Article

Authors: Datta, Ayan | Talukdar, Veera | Konar, Amit | Jain, Lakhmi C.

Affiliations: Dept. of Electronics and Telecommunication Engineering, Jadavpur University, Kolkata 700032, India | Indian Institute of Social Welfare and Buisness, Management,College Square West, Kolkata-700073, India | Knowledge-Based Intelligent Engineering Systems Centre, University of South Australia, Mawson Lakes Campus, Adelaide, SA 5095, Australia

Note: [] Corresponding author. E-mail: ayandatta_ece@yahoo.co.in

Abstract: The paper provides an alternative approach to protein structural class prediction employing artificial neural network. Existing works on protein structural class prediction are computationally intensive. The method employs SOFM for extraction of representative feature vectors, for the four different structural classes and then uses Principal Component Analysis for finding optimum feature vector dimension. Nearest neighborhood classification technique is finally utilised; to classify these protein datapoints to their respective classes. The proposed work presented in this paper, maintains the same level of classification accuracy in minimum computation time, as it employs most prominent and reduced number of feature set for classification.

Keywords: Bioinformatics, molecular biology, bio-computing, Evolutionary algorithm, soft computing, swarm intelligence, artificial neural network, protein folding, protein structure prediction

DOI: 10.3233/IFS-2009-0415

Journal: Journal of Intelligent & Fuzzy Systems, vol. 20, no. 1-2, pp. 61-71, 2009