Selene: a PyTorch-based deep learning library for sequence data View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2019-04

AUTHORS

Kathleen M. Chen, Evan M. Cofer, Jian Zhou, Olga G. Troyanskaya

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. More... »

PAGES

315-318

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41592-019-0360-8

DOI

http://dx.doi.org/10.1038/s41592-019-0360-8

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1113051253

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/30923381


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