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The Hybrid Method of Fuzzy Feed-Forward Neural Network for Predicting Protein Secondary Structure

DOI: 10.5539/cis.v6n1p133

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Abstract:

With respect to the fact that the prediction of Protein secondary structure based on amino acids is very important, therefore, this study tries to present a new method based on the fuzzy combinational structure of a set of feed-forward neural networks so that the prediction accuracy of Protein secondary structure can be improved compared with the existing methods. Neural networks used in this paper are based on time windows; also, different methods have been established and trained to infer the three states of alpha- helix, beta- sheet and coils from DSSP results, and finally, combining the results of the abovementioned networks in a fuzzy manner, the prediction method of Protein secondary structure based on neural network has been improved. It should be noted that in this paper, CB513 and RS126 data sets which are valid data sets in evaluating prediction methods of Protein secondary structure known in research studies in this area have been used to train and evaluate the proposed method.

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