Convolutional LSTM Networks for Subcellular Localization of Proteins

Søren Kaae Sønderby; Casper Kaae Sønderby; Henrik Nielsen; Ole Winther

doi:10.1007/978-3-319-21233-3_6

Convolutional LSTM Networks for Subcellular Localization of Proteins

Søren Kaae Sønderby, Casper Kaae Sønderby, Henrik Nielsen, Ole Winther

University of Copenhagen

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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Abstract

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.

Original language	English
Title of host publication	Algorithms for Computational Biology : Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings
Editors	Adrian-Horia Dediu, Francisco Hernández-Quiroz, Carlos Martín-Vide, David A. Rosenblueth
Number of pages	13
Volume	9199
Publisher	Springer
Publication date	2015
Pages	68-80
ISBN (Print)	978-3-319-21232-6
ISBN (Electronic)	978-3-319-21233-3
DOIs	https://doi.org/10.1007/978-3-319-21233-3_6
Publication status	Published - 2015
Event	2nd International Conference on Algorithms for Computational Biology - Mexico City, Mexico Duration: 4 Aug 2015 → 5 Aug 2015 Conference number: 2 http://grammars.grlmc.com/alcob2015/

Conference

Conference	2nd International Conference on Algorithms for Computational Biology
Number	2
Country/Territory	Mexico
City	Mexico City
Period	04/08/2015 → 05/08/2015
Internet address	http://grammars.grlmc.com/alcob2015/

Series	Lecture Notes in Computer Science
ISSN	0302-9743

Keywords

Subcellular location
Machine learning
LSTM
RNN
Neural networks
Deep learning
Convolutional networks

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10.1007/978-3-319-21233-3_6

Convolutional LSTMAccepted author manuscript, 1.49 MB

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Sønderby, S. K., Sønderby, C. K., Nielsen, H., & Winther, O. (2015). Convolutional LSTM Networks for Subcellular Localization of Proteins. In A.-H. Dediu, F. Hernández-Quiroz, C. Martín-Vide, & D. A. Rosenblueth (Eds.), Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings (Vol. 9199, pp. 68-80). Springer. https://doi.org/10.1007/978-3-319-21233-3_6

Sønderby, Søren Kaae ; Sønderby, Casper Kaae ; Nielsen, Henrik et al. / Convolutional LSTM Networks for Subcellular Localization of Proteins. Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings. editor / Adrian-Horia Dediu ; Francisco Hernández-Quiroz ; Carlos Martín-Vide ; David A. Rosenblueth. Vol. 9199 Springer, 2015. pp. 68-80 (Lecture Notes in Computer Science).

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title = "Convolutional LSTM Networks for Subcellular Localization of Proteins",

abstract = "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.",

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Sønderby, SK, Sønderby, CK, Nielsen, H & Winther, O 2015, Convolutional LSTM Networks for Subcellular Localization of Proteins. in A-H Dediu, F Hernández-Quiroz, C Martín-Vide & DA Rosenblueth (eds), Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings. vol. 9199, Springer, Lecture Notes in Computer Science, pp. 68-80, 2nd International Conference on Algorithms for Computational Biology, Mexico City, Mexico, 04/08/2015. https://doi.org/10.1007/978-3-319-21233-3_6

Convolutional LSTM Networks for Subcellular Localization of Proteins. / Sønderby, Søren Kaae; Sønderby, Casper Kaae; Nielsen, Henrik et al.
Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings. ed. / Adrian-Horia Dediu; Francisco Hernández-Quiroz; Carlos Martín-Vide; David A. Rosenblueth. Vol. 9199 Springer, 2015. p. 68-80 (Lecture Notes in Computer Science).

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

TY - GEN

T1 - Convolutional LSTM Networks for Subcellular Localization of Proteins

AU - Sønderby, Søren Kaae

AU - Sønderby, Casper Kaae

AU - Nielsen, Henrik

AU - Winther, Ole

N1 - Conference code: 2

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N2 - 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.

AB - 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.

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KW - Machine learning

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KW - RNN

KW - Neural networks

KW - Deep learning

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DO - 10.1007/978-3-319-21233-3_6

M3 - Article in proceedings

SN - 978-3-319-21232-6

VL - 9199

T3 - Lecture Notes in Computer Science

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BT - Algorithms for Computational Biology

A2 - Dediu, Adrian-Horia

A2 - Hernández-Quiroz, Francisco

A2 - Martín-Vide, Carlos

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PB - Springer

T2 - 2nd International Conference on Algorithms for Computational Biology

Y2 - 4 August 2015 through 5 August 2015

ER -

Sønderby SK, Sønderby CK, Nielsen H , Winther O. Convolutional LSTM Networks for Subcellular Localization of Proteins. In Dediu AH, Hernández-Quiroz F, Martín-Vide C, Rosenblueth DA, editors, Algorithms for Computational Biology: Second International Conference, AlCoB 2015, Mexico City, Mexico, August 4-5, 2015, Proceedings. Vol. 9199. Springer. 2015. p. 68-80. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-21233-3_6

Convolutional LSTM Networks for Subcellular Localization of Proteins

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