Convolutional LSTM networks for subcellular localization of proteins

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 -