An introduction to Deep learning on biological sequence data - Examples and solutions.
Jurtz, V. I., Rosenberg Johansen, A., Nielsen, M., Almagro Armenteros, J. J., Nielsen, H., Kaae Sonderby, C., Winther, O. and Kaae Sonderby, S.
Department of Bio and Health Informatics, Technical University of Denmark.
Department of Applied Mathematics and Computer Science, Technical University of Denmark.
Instituto de Investigaciones Biotecnologicas, Universidad Nacional de San Martin, Buenos Aires, Argentina.
Department of Biology, University of Copenhagen.
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: All implementations and datasets are available online to the scientific community at https://github.com/vanessajurtz/lasagne4bio . Supplementary information: Supplementary data are available at Bioinformatics online.
Bioinformatics : en prensa (2017)