Convolutional LSTM networks for subcellular localization of proteins

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

Standard

Convolutional LSTM networks for subcellular localization of proteins. / Sønderby, Søren Kaae; Sønderby, Casper Kaae; Nielsen, Henrik; Winther, Ole.

Algorithms for Computational Biology. ed. / Adrian-Horia Dediu; Francisco Hernández-Quiroz; Carlos Martín-Vide; David A. Rosenblueth. Springer, 2015. p. 68-80 (Lecture notes in computer science, Vol. 9199).

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

Harvard

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. Springer, Lecture notes in computer science, vol. 9199, pp. 68-80, 2nd International Conference on Algorithms for Computational Biology, AlCoB 2015, Mexico City, Mexico, 04/08/2015. https://doi.org/10.1007/978-3-319-21233-3_6

APA

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 (pp. 68-80). Springer. Lecture notes in computer science Vol. 9199 https://doi.org/10.1007/978-3-319-21233-3_6

Vancouver

Sønderby SK, Sønderby CK, Nielsen H, Winther O. Convolutional LSTM networks for subcellular localization of proteins. In Dediu A-H, Hernández-Quiroz F, Martín-Vide C, Rosenblueth DA, editors, Algorithms for Computational Biology. Springer. 2015. p. 68-80. (Lecture notes in computer science, Vol. 9199). https://doi.org/10.1007/978-3-319-21233-3_6

Author

Sønderby, Søren Kaae ; Sønderby, Casper Kaae ; Nielsen, Henrik ; Winther, Ole. / Convolutional LSTM networks for subcellular localization of proteins. Algorithms for Computational Biology. editor / Adrian-Horia Dediu ; Francisco Hernández-Quiroz ; Carlos Martín-Vide ; David A. Rosenblueth. Springer, 2015. pp. 68-80 (Lecture notes in computer science, Vol. 9199).

Bibtex

@inproceedings{e86d47a1de3043589ed810d326efa3cc,

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.",

keywords = "Convolutional networks, Deep learning, LSTM, Machine learning, Neural networks, RNN, Subcellular location",

author = "S{\o}nderby, {S{\o}ren Kaae} and S{\o}nderby, {Casper Kaae} and Henrik Nielsen and Ole Winther",

year = "2015",

doi = "10.1007/978-3-319-21233-3_6",

language = "English",

isbn = "978-3-319-21232-6",

series = "Lecture notes in computer science",

publisher = "Springer",

pages = "68--80",

editor = "Dediu, {Adrian-Horia } and Francisco Hern{\'a}ndez-Quiroz and Mart{\'i}n-Vide, {Carlos } and Rosenblueth, {David A. }",

booktitle = "Algorithms for Computational Biology",

address = "Switzerland",

note = "2nd International Conference on Algorithms for Computational Biology, AlCoB 2015 ; Conference date: 04-08-2015 Through 05-08-2015",

}

RIS

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

PY - 2015

Y1 - 2015

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.

KW - Convolutional networks

KW - Deep learning

KW - LSTM

KW - Machine learning

KW - Neural networks

KW - RNN

KW - Subcellular location

U2 - 10.1007/978-3-319-21233-3_6

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

M3 - Article in proceedings

AN - SCOPUS:84951119143

SN - 978-3-319-21232-6

T3 - Lecture notes in computer science

SP - 68

EP - 80

BT - Algorithms for Computational Biology

A2 - Dediu, Adrian-Horia

A2 - Hernández-Quiroz, Francisco

A2 - Martín-Vide, Carlos

A2 - Rosenblueth, David A.

PB - Springer

T2 - 2nd International Conference on Algorithms for Computational Biology, AlCoB 2015

Y2 - 4 August 2015 through 5 August 2015

ER -

ID: 153446256