Protein Secondary Structure Prediction with Long Short Term Memory Networks

Søren Kaae Sønderby; Ole Winther

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arXiv · 1412.7828

Protein Secondary Structure Prediction with Long Short Term Memory Networks

Søren Kaae Sønderby·Ole Winther

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Abstract

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.

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q-bio.QM cs.LG cs.NE

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BibTeX RIS

Søren Kaae Sønderby, Ole Winther. 2014-12-25. Protein Secondary Structure Prediction with Long Short Term Memory Networks. https://arxiv.org/abs/1412.7828

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