Title | Selene: a PyTorch-based deep learning library for sequence data. |
Publication Type | Journal Article |
Year of Publication | 2019 |
Authors | Chen, KM, Cofer, EM, Zhou, J, Troyanskaya, OG |
Journal | Nat Methods |
Volume | 16 |
Issue | 4 |
Pagination | 315-318 |
Date Published | 2019 04 |
ISSN | 1548-7105 |
Keywords | Algorithms, 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 |
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. |
DOI | 10.1038/s41592-019-0360-8 |
Alternate Journal | Nat. Methods |
PubMed ID | 30923381 |
Grant List | HHSN272201000054C / 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 |