Selene: a PyTorch-based deep learning library for sequence-level data
Selene: a PyTorch-based deep learning library for sequence-level data
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 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.
Cofer Evan M.、Zhou Jian、Troyanskaya Olga G.、Chen Kathleen M.
Lewis-Sigler Institute for Integrative Genomics, Princeton University||Graduate Program in Quantitative and Computational Biology, Princeton UniversityFlatiron Institute||Lewis-Sigler Institute for Integrative Genomics, Princeton UniversityFlatiron Institute||Lewis-Sigler Institute for Integrative Genomics, Princeton University||Department of Computer Science, Princeton UniversityFlatiron Institute
生物科学研究方法、生物科学研究技术计算技术、计算机技术分子生物学
Cofer Evan M.,Zhou Jian,Troyanskaya Olga G.,Chen Kathleen M..Selene: a PyTorch-based deep learning library for sequence-level data[EB/OL].(2025-03-28)[2025-05-23].https://www.biorxiv.org/content/10.1101/438291.点此复制
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