Conference paper · Book chapter · Preprint article
Convolutional LSTM Networks for Subcellular Localization of Proteins
University of Copenhagen1
Department of Systems Biology, Technical University of Denmark2
Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark3
Functional Human Variation, Center for Biological Sequence Analysis, Department of Systems Biology, Technical University of Denmark4
Department of Applied Mathematics and Computer Science, Technical University of Denmark5
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark6
Machine learning is widely used to analyze biological sequence data. Non-sequential models such as SVMs or feed-forward neural networks are often used although they have no natural way of handling sequences of varying length. Recurrent neural networks such as the long short term memory (LSTM) model on the other hand are designed to handle sequences.
In this study we demonstrate that LSTM networks predict the subcellular location of proteins given only the protein sequence with high accuracy (0.902) outperforming current state of the art algorithms. We further improve the performance by introducing convolutional filters and experiment with an attention mechanism which lets the LSTM focus on specific parts of the protein.
Lastly we introduce new visualizations of both the convolutional filters and the attention mechanisms and show how they can be used to extract biologically relevant knowledge from the LSTM networks.
Language: | English |
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Publisher: | Springer |
Year: | 2015 |
Pages: | 68-80 |
Proceedings: | 2nd International Conference on Algorithms for Computational Biology |
Series: | Lecture Notes in Computer Science |
Journal subtitle: | Second International Conference, Alcob 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings |
ISBN: | 331921232X , 331921232x , 3319212338 , 9783319212326 and 9783319212333 |
ISSN: | 16113349 and 03029743 |
Types: | Conference paper , Book chapter and Preprint article |
DOI: | 10.1007/978-3-319-21233-3_6 |
ORCIDs: | Winther, Ole and Nielsen, Henrik |