SetBERT: the deep learning platform for contextualized embeddings and explainable predictions from high-throughput sequencing
All source code is available at https://github.com/DLii-Research/setbert. SetBERT may be used through the q2-deepdna QIIME 2 plugin whose source code is available at https://github.com/DLii-Research/q2-deepdna.
Authors
- Nicholas A. Barber (ORCID: https://orcid.org/0000-0003-1653-0009)
- Kateryna Zhalnina (ORCID: https://orcid.org/0000-0002-1893-1888)
- Joshua L. Phillips (ORCID: https://orcid.org/0000-0002-4619-6083)
- Wesley D. Swingley (ORCID: https://orcid.org/0000-0001-7734-5179)
- N. Reed Alexander (ORCID: https://orcid.org/0009-0004-7271-5520)
- Donald M. Walker (ORCID: https://orcid.org/0000-0003-3119-8809)
- Albina Khasanova (ORCID: https://orcid.org/0000-0001-6652-012X)
- David W. Ludwig (ORCID: https://orcid.org/0009-0001-5851-7964)
- Christopher Guptil
- Edi M -L Wipf (ORCID: https://orcid.org/0000-0002-1025-9310)
Institutions
- Northern Illinois University (US)
- Lawrence Berkeley National Laboratory (US)
- Middle Tennessee State University (US)
- San Diego State University (US)
- Miami University (US)
Publication Details
- Journal
- Bioinformatics
- Published
- 2025-06-24
- DOI
- https://doi.org/10.1093/bioinformatics/btaf370
- Primary Topic
- Bioinformatics and Genomic Networks
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- National Science Foundation
- U.S. Department of Energy
- Tennessee Wildlife Resources Agency
- Ministry of Education, India
- Office of Science
- Lawrence Berkeley National Laboratory