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

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 sequences. We demonstrate how Selen...

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Detalles Bibliográficos
Autores principales: Chen, Kathleen M., Cofer, Evan M., Zhou, Jian, Troyanskaya, Olga G.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2019
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148117/
https://www.ncbi.nlm.nih.gov/pubmed/30923381
http://dx.doi.org/10.1038/s41592-019-0360-8
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author Chen, Kathleen M.
Cofer, Evan M.
Zhou, Jian
Troyanskaya, Olga G.
author_facet Chen, Kathleen M.
Cofer, Evan M.
Zhou, Jian
Troyanskaya, Olga G.
author_sort Chen, Kathleen M.
collection PubMed
description 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 sequences. We demonstrate 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.
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spelling pubmed-71481172020-04-10 Selene: a PyTorch-based deep learning library for sequence data Chen, Kathleen M. Cofer, Evan M. Zhou, Jian Troyanskaya, Olga G. Nat Methods Article 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 sequences. We demonstrate 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. 2019-03-28 2019-04 /pmc/articles/PMC7148117/ /pubmed/30923381 http://dx.doi.org/10.1038/s41592-019-0360-8 Text en Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:http://www.nature.com/authors/editorial_policies/license.html#terms
spellingShingle Article
Chen, Kathleen M.
Cofer, Evan M.
Zhou, Jian
Troyanskaya, Olga G.
Selene: a PyTorch-based deep learning library for sequence data
title Selene: a PyTorch-based deep learning library for sequence data
title_full Selene: a PyTorch-based deep learning library for sequence data
title_fullStr Selene: a PyTorch-based deep learning library for sequence data
title_full_unstemmed Selene: a PyTorch-based deep learning library for sequence data
title_short Selene: a PyTorch-based deep learning library for sequence data
title_sort selene: a pytorch-based deep learning library for sequence data
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7148117/
https://www.ncbi.nlm.nih.gov/pubmed/30923381
http://dx.doi.org/10.1038/s41592-019-0360-8
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AT troyanskayaolgag seleneapytorchbaseddeeplearninglibraryforsequencedata

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