TRACE: applying AI language models to extract ancestry information from curated biomedical literature
Year: 2025
Type: article
Abstract: Introduction: Ancestry reporting is essential to ensure transparency and proper representation in biomedical studies. However, manually extracting this information from study texts is time-consuming and inefficient. In this paper, we present TRACE (Tool for Researching Ancestry and Cell Extraction), powered by GPT-4 and web-crawling, to automate ancestry identification by detecting cell lines or cultures in texts and tracing their ancestry. Methods: TRACE extracts cell lines and primary cultures from research articles and follows web sources to determine their ancestry. We compared TRACE's outputs to a manually generated database to confirm its performance in identifying and verifying ancestry information. Results: The results reveal an overrepresentation of European/White samples and significant underreporting. TRACE enables large-scale, systematic ancestry analysis-a valuable resource for researchers and agencies assessing biases in sample selection. Conclusions: As an open-source tool, TRACE it facilitates broader use to evaluate and improve ancestry representation in biomedical research.
Source: Frontiers in Digital Health
Authors: Alison M. Veintimilla, Chintan K. Acharya, Connie J. Mulligan, Ruogu Fang, Erika Moore
Institutions: University of Maryland, College Park, University of Florida
Language: English
Topic: Race, Genetics, and Society
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,077
Funder: Lupus Research Alliance
OpenAlex ID: W4414364393