TRACE:应用人工智能语言模型从精心整理的生物医学文献中提取祖先信息。

IF 3.2 Q1 HEALTH CARE SCIENCES & SERVICES
Frontiers in digital health Pub Date : 2025-09-19 eCollection Date: 2025-01-01 DOI:10.3389/fdgth.2025.1608370
Alison M Veintimilla, Chintan K Acharya, Connie J Mulligan, Ruogu Fang, Erika Moore
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引用次数: 0

摘要

祖先报告是必不可少的,以确保透明度和适当的代表性在生物医学研究。然而,手动从学习文本中提取这些信息既耗时又低效。在本文中,我们提出了TRACE(研究祖先和细胞提取工具),由GPT-4和网络爬行提供动力,通过检测文本中的细胞系或培养物并追踪其祖先来自动识别祖先。方法:TRACE从研究文章中提取细胞系和原代培养物,并根据网络来源确定其祖先。我们将TRACE的输出与手动生成的数据库进行比较,以确认其在识别和验证祖先信息方面的性能。结果:结果揭示了欧洲/白人样本的过度代表性和显著的低报。TRACE可以进行大规模、系统的祖先分析,这是研究人员和机构评估样本选择偏差的宝贵资源。结论:作为一个开源工具,TRACE有助于在生物医学研究中更广泛地使用来评估和改善祖先代表性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
TRACE: applying AI language models to extract ancestry information from curated biomedical literature.

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.

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CiteScore
4.20
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