@article{2582,
  keywords = {Mutation, Humans, Computational Biology, Genomics, Algorithms, Models, Statistical, Programming Languages, Software, Sequence Analysis, DNA, Area Under Curve, Mutagenesis, Gene Library, Neural Networks (Computer), Normal Distribution, Deep Learning, Alzheimer Disease},
  author = {Kathleen Chen and Evan Cofer and Jian Zhou and Olga Troyanskaya},
  title = {Selene: a PyTorch-based deep learning library for sequence data.},
  abstract = {<p>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.</p>
},
  year = {2019},
  journal = {Nat Methods},
  volume = {16},
  pages = {315-318},
  month = {04/2019},
  issn = {1548-7105},
  doi = {10.1038/s41592-019-0360-8},
  language = {eng},
}