Dataset Information

An introduction to deep learning on biological sequence data: examples and solutions.

ABSTRACT:

Motivation

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.

Results

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.

Availability and implementation

All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio.

Contact

skaaesonderby@gmail.com.

Supplementary information

Supplementary data are available at Bioinformatics online.

SUBMITTER: Jurtz VI

PROVIDER: S-EPMC5870575 | biostudies-literature | 2017 Nov

REPOSITORIES: biostudies-literature

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Publications

An introduction to deep learning on biological sequence data: examples and solutions.

Jurtz Vanessa Isabell VI Johansen Alexander Rosenberg AR Nielsen Morten M Almagro Armenteros Jose Juan JJ Nielsen Henrik H Sønderby Casper Kaae CK Winther Ole O Sønderby Søren Kaae SK

Bioinformatics (Oxford, England) 20171101 22

<h4>Motivation</h4>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 d ...[more]

PMID: 28961695

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Dataset Information

An introduction to deep learning on biological sequence data: examples and solutions.

Motivation

Results

Availability and implementation

Contact

Supplementary information

Publications

An introduction to deep learning on biological sequence data: examples and solutions.

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