Comparing approaches to predict transmembrane domains in protein sequences
Proceedings of the 2005 ACM symposium on Applied computing
Dimensional reduction in the protein secondary structure prediction: non-linear method improvements
International Journal of Computational Intelligence in Bioinformatics and Systems Biology
Prediction of protein secondary structure using nonlinear method
ICONIP'06 Proceedings of the 13th international conference on Neural information processing - Volume Part III
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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 result 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 intrincate 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.