TitleSelene: a PyTorch-based deep learning library for sequence data.
Publication TypeJournal Article
Year of Publication2019
AuthorsChen, KM, Cofer, EM, Zhou, J, Troyanskaya, OG
JournalNat Methods
Date Published2019 04
KeywordsAlgorithms, Alzheimer Disease, Area Under Curve, Computational Biology, Deep Learning, Gene Library, Genomics, Humans, Models, Statistical, Mutagenesis, Mutation, Neural Networks (Computer), Normal Distribution, Programming Languages, Sequence Analysis, DNA, Software

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.

Alternate JournalNat. Methods
PubMed ID30923381
Grant ListHHSN272201000054C / AI / NIAID NIH HHS / United States
R01 HG005998 / HG / NHGRI NIH HHS / United States
T32 HG003284 / HG / NHGRI NIH HHS / United States
U54 HL117798 / HL / NHLBI NIH HHS / United States