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Combining few neural networks for effective secondary structure prediction

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3 Author(s)
K. S. Guimaraes ; Center of Informatics, UFPE, Recife, Brazil ; J. C. B. Melo ; G. D. C. Cavalcanti

The prediction of secondary structure is treated with a simple and efficient method. Combining only three neural networks, an average Q3 accuracy prediction by residues of 75.93% is achieved. This value is better than the best results reported on the same test and training database, CB396, using the same validation method. For a second database, RS126, an average Q3 accuracy of 74.13% is attained, which is better than each individual method, being defeated only by CONSENSUS, a rather intricate engine, which is a combination of several methods. The networks are trained with RPROP an efficient variation of the back-propagation algorithm. Five combination rules are applied independently afterwards. Each one increases the accuracy of prediction by at least 1%, due to the fact that each network used converges to a different local minimum. The Product rule derives the best results. The predictor described here can be accessed at

Published in:

Bioinformatics and Bioengineering, 2003. Proceedings. Third IEEE Symposium on

Date of Conference:

10-12 March 2003