BetaAlign: a deep learning approach for multiple sequence alignment
Datasets used in this work are available on HuggingFace (Wolf et al. Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations. p.38-45. 2020) at: https://huggingface.co/dotan1111. Source code is available at: https://github.com/idotan286/SimulateAlignments.
Authors
- Michael Alburquerque
- Elya Wygoda
- Noa Ecker (ORCID: https://orcid.org/0009-0005-6785-7353)
- Yonatan Belinkov
- Edo Dotan
- Tal Pupko (ORCID: https://orcid.org/0000-0001-9463-2575)
- Oren Avram (ORCID: https://orcid.org/0000-0003-1984-2139)
Institutions
- University of California, Los Angeles (US)
- Tel Aviv University (IL)
- Technion – Israel Institute of Technology (IL)
Publication Details
- Journal
- Bioinformatics
- Published
- 2024-12-26
- DOI
- https://doi.org/10.1093/bioinformatics/btaf009
- Citations
- 5
- Primary Topic
- Genomics and Phylogenetic Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.17
Funders
- Tel Aviv University
- Azrieli Foundation