Protein Secondary Structure Prediction with Long Short Term Memory Networks
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.
Cite
@article{arxiv.1412.7828,
title = {Protein Secondary Structure Prediction with Long Short Term Memory Networks},
author = {Søren Kaae Sønderby and Ole Winther},
journal= {arXiv preprint arXiv:1412.7828},
year = {2015}
}
Comments
v2: adds larger network with slightly better results, update author affiliations