Pruning neural networks for protein secondary structure prediction

Secondary structure prediction is an effective approach in deducing the three dimensional structure and functions of proteins. Although the multilayer neural network is currently used for the prediction, appropriate determination of the network size is yet an important factor in improving the performance of the network. In this work, two systematic approaches for pruning the oversized multilayer perceptron neural networks (MLP-NN) are proposed to determine the optimum size of the hidden layer. Using the RS126 dataset in seven-fold cross-validation, the percentage accuracy of the prediction reaches to 75.38.