Capturing Long-Term Dependencies for Protein Secondary Structure Prediction
نویسندگان
چکیده
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 use SMRNNs to replace the standard RNNs in BRNN. The resulting architecture is called bidirectional segmented-memory recurrent neural network(BSMRNN). Our experiment with BSMRNN for protein secondary structure prediction on the RS126 set indicates improvement in the prediction accuracy.
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