Protein Secondary Structure Prediction with Long Short Term Memory Networks
This work addresses a classical bioinformatics problem for researchers in computational biology, but it is incremental as it builds on existing recurrent neural network methods.
The authors tackled protein secondary structure prediction by using a bidirectional LSTM network, achieving a performance of 0.674 on the CB513 dataset, which is better than the state-of-the-art result of 0.664.
Prediction of protein secondary structure from the amino acid sequence is a classical bioinformatics problem. Common methods use feed forward neural networks or SVMs combined with a sliding window, as these models does not naturally handle sequential data. Recurrent neural networks are an generalization of the feed forward neural network that naturally handle sequential data. We use a bidirectional recurrent neural network with long short term memory cells for prediction of secondary structure and evaluate using the CB513 dataset. On the secondary structure 8-class problem we report better performance (0.674) than state of the art (0.664). Our model includes feed forward networks between the long short term memory cells, a path that can be further explored.