Deep learning: new computational modelling techniques for genomics.

Journal: Nature reviews. Genetics
Published Date:

Abstract

As a data-driven science, genomics largely utilizes machine learning to capture dependencies in data and derive novel biological hypotheses. However, the ability to extract new insights from the exponentially increasing volume of genomics data requires more expressive machine learning models. By effectively leveraging large data sets, deep learning has transformed fields such as computer vision and natural language processing. Now, it is becoming the method of choice for many genomics modelling tasks, including predicting the impact of genetic variation on gene regulatory mechanisms such as DNA accessibility and splicing.

Authors

  • Gökcen Eraslan
    Institute of Computational Biology, Helmholtz Zentrum München, Neuherberg, Germany.
  • Žiga Avsec
    Department of Informatics, Technical University of Munich, 85748 Garching, Germany.
  • Julien Gagneur
    Department of Informatics, Technical University of Munich, 85748 Garching, Germany.
  • Fabian J Theis
    Institute of Computational Biology, Helmholtz Zentrum München, German Research Center for Environmental Health, Munich, Germany.