Capturing Long-Term Dependencies for Protein Secondary Structure Prediction

Abstract

Bidirectional recurrent neural network (BRNN) is a noncausal system that captures both upstream and downstream information for protein secondary structure prediction. Due to the problem of vanishing gradients, the BRNN can not learn remote information efficiently. To limit this problem, we propose segmented memory recurrent neural network (SMRNN) and obtain a bidirectional segmented-memory recurrent neural network (BSMRNN) by replacing the standard RNNs in BRNN with SMRNNs. Our experiment with BSMRNN for protein secondary structure prediction on the RS126 set indicates improvement in the prediction accuracy.