Selene: a PyTorch-based deep learning library for sequence data.

Journal: Nature methods
Published Date:

Abstract

To enable the application of deep learning in biology, we present Selene (https://selene.flatironinstitute.org/), a PyTorch-based deep learning library for fast and easy development, training, and application of deep learning model architectures for any biological sequence data. We demonstrate on DNA sequences how Selene allows researchers to easily train a published architecture on new data, develop and evaluate a new architecture, and use a trained model to answer biological questions of interest.

Authors

  • Kathleen M Chen
    Department of Systems Pharmacology and Translational Therapeutics, University of Pennsylvania, Philadelphia, PA, USA.
  • Evan M Cofer
    Department of Computer Science, Trinity University, San Antonio, TX, USA.
  • Jian Zhou
    CTIQ, Canon Medical Research USA, Inc., Vernon Hills, 60061, USA.
  • Olga G Troyanskaya
    Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. ogt@cs.princeton.edu.