How to go with the flow: flow matching in bioinformatics and computational biology
Numerous problems in bioinformatics and computational biology can be framed as a task of learning a mapping from one state of a biological system to another relevant state or to explore novel data points across biologically-constrained spaces. However
Authors
- Jianlin Jack Cheng (ORCID: https://orcid.org/0000-0003-0305-2853)
- Aditi S. Krishnapriyan (ORCID: https://orcid.org/0000-0003-3472-6080)
- Alexander Tong (ORCID: https://orcid.org/0000-0002-2031-4096)
- Frimpong Boadu (ORCID: https://orcid.org/0000-0002-4464-6191)
- Joel Selvaraj (ORCID: https://orcid.org/0000-0003-0068-3262)
- Alex Morehead (ORCID: https://orcid.org/0000-0002-0586-6191)
- Lazar Atanackovic
- Yanli Wang (ORCID: https://orcid.org/0000-0002-7576-3235)
- Akshata Hegde (ORCID: https://orcid.org/0009-0009-7077-9420)
Institutions
- Lawrence Berkeley National Laboratory (US)
- Vector Institute (CA)
- NextGen Aeronautics (United States) (US)
- Mila - Quebec Artificial Intelligence Institute (CA)
Publication Details
- Published
- 2025-08-29
- DOI
- https://doi.org/10.22541/au.175382408.89466370/v2
- Primary Topic
- Data Mining Algorithms and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- U.S. Department of Energy
- National Energy Research Scientific Computing Center