Protein secondary structure prediction using a fully complex-valued relaxation network

Knowledge of the various protein functions is essential to understand the manifestation of diseases and develop suitable drugs to alleviate them. As proteins form conformational patterns like α-helix and β-strands that eventually fold up into 3-D structure, prediction of the secondary structure of proteins is an important intermediate step in understanding the final structure of proteins that are vital for performing biological functions. Thus, there is a need to predict the secondary structure of proteins accurately and efficiently. Recent studies in machine learning have shown that complex-valued neural networks have better decision making ability than real-valued networks. Therefore, we use a Fully Complex-valued Relaxation Network (FCRN) classifier to predict the secondary structure of proteins in this paper. FCRN classifier is a single hidden layer neural network classifier with nonlinear input, hidden and output layers. The neurons in the input layer convert the real-valued input features to the Complex domain using a circular transformation. The neurons in the hidden layer employ a fully complex-valued sech activation function and those in the output layer employ the fully complex-valued exp activation function. For constant random input parameters, FCRN estimates the output weights corresponding to the minimum energy point of a logarithmic error function that represents both the magnitude and phase error explicitly. The prediction performance of FCRN is compared against the best results available in the literature for this problem. Our results show that FCRN presents higher or comparable prediction accuracy than other classifiers available in the literature.