Convolution-Bidirectional Temporal Convolutional Network for Protein Secondary Structure Prediction

Abstract

As a basic feature extraction method, convolutional neural networks have some information loss problems when dealing with sequence problems, and a temporal convolutional network can compensate for this problem. Howerover, ordinary temporal convolutional networks can not deal well protein secondary structure prediction because of their one-way analysis. Therefore, we propose an integrated deep learning model called Convolutional-Bidirectional Temporal Convolutional Network. for 3-state and 8-state protein secondary structure predictions based on a convolutional neural network and bidirectional temporal convolutional networks. Because the model combines the advantages of the convolutional neural network and bidirectional temporal convolution network, it can not only capture the local correlation in the amino acid sequence but also analyse the long-distance interaction in the amino acid sequence. Therefore, this model can effectively improve the accuracy of protein secondary structure predictions. The experimental results show that the combination of convolutional neural network and bidirectional temporal convolutional network is effective for predicting protein secondary structure.