Abstract
| Original language | English |
|---|---|
| Journal | Bioinformatics |
| Volume | 33 |
| Issue number | 22 |
| Pages (from-to) | 3685-3690 |
| ISSN | 1367-4803 |
| DOIs | |
| Publication status | Published - 2017 |
Cite this
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An introduction to Deep learning on biological sequence data - Examples and solutions. / Jurtz, Vanessa Isabell; Johansen, Alexander Rosenberg; Nielsen, Morten; Almagro Armenteros, Jose Juan; Nielsen, Henrik; Kaae Sønderby, Casper; Winther, Ole; Kaae Sønderby, Søren.
In: Bioinformatics, Vol. 33, No. 22, 2017, p. 3685-3690.Research output: Contribution to journal › Journal article › Research › peer-review
TY - JOUR
T1 - An introduction to Deep learning on biological sequence data - Examples and solutions
AU - Jurtz, Vanessa Isabell
AU - Johansen, Alexander Rosenberg
AU - Nielsen, Morten
AU - Almagro Armenteros, Jose Juan
AU - Nielsen, Henrik
AU - Kaae Sønderby, Casper
AU - Winther, Ole
AU - Kaae Sønderby, Søren
PY - 2017
Y1 - 2017
N2 - Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio . Supplementary data are available at Bioinformatics online.
AB - Deep neural network architectures such as convolutional and long short-term memory networks have become increasingly popular as machine learning tools during the recent years. The availability of greater computational resources, more data, new algorithms for training deep models and easy to use libraries for implementation and training of neural networks are the drivers of this development. The use of deep learning has been especially successful in image recognition; and the development of tools, applications and code examples are in most cases centered within this field rather than within biology. Here, we aim to further the development of deep learning methods within biology by providing application examples and ready to apply and adapt code templates. Given such examples, we illustrate how architectures consisting of convolutional and long short-term memory neural networks can relatively easily be designed and trained to state-of-the-art performance on three biological sequence problems: prediction of subcellular localization, protein secondary structure and the binding of peptides to MHC Class II molecules. All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio . Supplementary data are available at Bioinformatics online.
U2 - 10.1093/bioinformatics/btx531
DO - 10.1093/bioinformatics/btx531
M3 - Journal article
C2 - 28961695
VL - 33
SP - 3685
EP - 3690
JO - Bioinformatics
JF - Bioinformatics
SN - 1367-4803
IS - 22
ER -