Study of Neural Network Methods in Predicting Protein Secondary Structures

In this paper,five neural network models,such as back-propagation neural network(BPNN),radial basis neural network(RBFNN),generalized regression neural network(GRNN),cascade forward backpropagation neural network(CFNN) and Elman backpropagation neural network(ELMNN),have been evaluated in predicting protein secondary structures.The prediction accuracy of GRNN is better than the others.In addition,some affecting factors(the training sets and the parameters of network) are also discussed.