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Nat Methods. 2019 Apr;16(4):315-318. doi: 10.1038/s41592-019-0360-8. Epub 2019 Mar 28.

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

Author information

1
Flatiron Institute, Simons Foundation, New York, NY, USA.
2
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA.
3
Graduate Program in Quantitative and Computational Biology, Princeton University, Princeton, NJ, USA.
4
Flatiron Institute, Simons Foundation, New York, NY, USA. ogt@cs.princeton.edu.
5
Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA. ogt@cs.princeton.edu.
6
Department of Computer Science, Princeton University, Princeton, NJ, USA. ogt@cs.princeton.edu.

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.

PMID:
30923381
DOI:
10.1038/s41592-019-0360-8
[Indexed for MEDLINE]

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