TRACE: applying AI language models to extract ancestry information from curated biomedical literature
As an open-source tool, TRACE it facilitates broader use to evaluate and improve ancestry representation in biomedical research.
Authors
- Ruogu Fang (ORCID: https://orcid.org/0000-0003-3980-3532)
- Erika Moore (ORCID: https://orcid.org/0000-0003-2192-6147)
- Alison M. Veintimilla (ORCID: https://orcid.org/0009-0003-4648-7539)
- Connie J. Mulligan (ORCID: https://orcid.org/0000-0002-4360-2402)
- Celin Acharya (ORCID: https://orcid.org/0000-0002-9885-2767)
Institutions
- University of Florida (US)
- University of Maryland, College Park (US)
Publication Details
- Journal
- Frontiers in Digital Health
- Published
- 2025-09-19
- DOI
- https://doi.org/10.3389/fdgth.2025.1608370
- Citations
- 1
- Primary Topic
- Cancer-related molecular mechanisms research
- Type
- article
- Field-Weighted Citation Impact
- 0.94
Funders
- Lupus Research Alliance