Secondary Structure Prediction of Protein using Resilient Back Propagation Learning Algorithm

Jyotshna Dongardive, Siby Abraham


The paper proposes a neural network based approach to predict secondary structure of protein. It uses Multilayer Feed Forward Network (MLFN) with resilient back propagation as the learning algorithm. Point Accepted Mutation (PAM) is adopted as the encoding scheme and CB396 data set is used for the training and testing of the network. Overall accuracy of the network has been experimentally calculated with different window sizes for the sliding window scheme and by varying the number of units in the hidden layer. The best results were obtained with eleven as the window size and seven as the number of units in the hidden layer.


Resilient back propagation, point accepted mutation, sliding window, Q3, hidden units.

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