Selene: a PyTorch-based deep learning library for sequence data. Author Kathleen Chen, Evan Cofer, Jian Zhou, Olga Troyanskaya Publication Year 2019 Type Journal Article 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. Keywords Mutation, Humans, Computational Biology, Genomics, Algorithms, Models, Statistical, Programming Languages, Software, Sequence Analysis, DNA, Area Under Curve, Mutagenesis, Gene Library, Neural Networks (Computer), Normal Distribution, Deep Learning, Alzheimer Disease Journal Nat Methods Volume 16 Issue 4 Pages 315-318 Date Published 04/2019 ISSN Number 1548-7105 DOI 10.1038/s41592-019-0360-8 Alternate Journal Nat. Methods PMID 30923381 PubMedGoogle ScholarBibTeXEndNote X3 XML