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Neural network based prediction of protein structure and Function: Comparison with other machine learning methods

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3 Author(s)
M. Michael Gromiha ; Computational Biology Research Center, National Institute of Advanced Industrial Science and Technology, AIST Tokyo Waterfront Bio-IT Research Building, 2-42 Aomi, Koto-ku, 135-0064, Japan ; Shandar Ahmad ; Makiko Suwa

We have utilized neural networks in different applications of bioinformatics such as discrimination of beta-barrel membrane proteins, mesophilic and thermophilic proteins, different folding types of globular proteins, different classes of transporter proteins and predicting the secondary structures of beta-barrel membrane proteins. In these methods, we have used the information about amino acid composition, neighboring residue information, inter-residue contacts and amino acid properties as features. We observed that the performance with neural networks is comparable to or better than other widely used machine learning techniques.

Published in:

2008 IEEE International Joint Conference on Neural Networks (IEEE World Congress on Computational Intelligence)

Date of Conference:

1-8 June 2008