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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1105

Title: Improved Protein Structural Class Prediction Using Genetic Algorithm and Artificial Immune System
Authors: Sahu, S S
Panda, G
Nanda, S Jagannath
Issue Date: 2009
Publisher: PSG College of Technology
Citation: World Congress on Nature and Biologically Inspired Computiing -NaBIC 2009, 9-11 Dec 2009, Coimbatore,India
Abstract: Predicting the structure of a protein from primary sequence is one of the challenging problems in Molecular biology. In this context, protein structural class information provides a key idea of their structure and also other features related to the biological function. In this paper we present a new optimization approach based on Genetic algorithm (GA) and artificial immune system (AIS) for predicting the protein structural class. It uses the maximum component coefficient principle in association with the amino acid composition feature vector to efficiently classify the protein structures. The effectiveness is evaluated by comparing the results with that obtained from other existing methods using a standard database. Especially for all and + class protein, the rate of accurate prediction by the proposed methods is much higher than their counterparts.
Description: Copyright for the paper belongs to proceedings publisher
URI: http://hdl.handle.net/2080/1105
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