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TRACE:应用AI语言模型从精选的生物医学文献中提取祖先信息

Alison M Veintimilla¹, Chintan K Acharya², Connie J Mulligan³

¹Fischell Department of Bioengineering, University of Maryland, College Park, MD, United States.

Frontiers in digital health

|October 6, 2025

概括

一个新的工具TRACE可以自动识别生物医学研究中的细胞系祖先. 这有助于研究人员系统地分析样本代表性和解决偏见,提高研究的透明度.

科学领域:

生物医学研究生物医学研究
基因组学就是基因组学.
生物信息学是一种生物信息学.

背景情况:

准确的祖先报告对于生物医学研究的透明度和代表性至关重要.
从研究文本中手动提取祖先数据是低效和耗时的.
对某些祖先的报道不足和过度报道可能会在研究结果中引入偏见.

研究的目的:

开发一个自动化工具,TRACE (Tool for Researching Ancestry and Cell Extraction),用于识别和追踪细胞系和初级培养的祖先.
提高从生物医学文献中提取祖先数据的效率和准确性.
促进研究中祖先代表性的大规模,系统的分析.

主要方法:

TRACE使用GPT-4和网络爬虫技术来检测研究文章中的细胞系或细胞培养.
该工具提取了已识别的细胞系和初级文化,并通过网络来源追踪它们的祖先.
通过将其输出与手动策划的祖先信息数据库进行比较,验证了TRACE的性能.

主要成果:

该研究发现,在分析的文献中,欧洲/白人样本的比例过高.
观察到大量的祖先信息报告不足.
TRACE在识别和验证祖先数据方面表现出有效性,使得系统分析成为可能.

结论:

TRACE为研究人员和机构提供了一个宝贵的资源,以评估样本选择中的偏见.
作为一个开源工具,TRACE促进了对生物医学研究中祖先代表性的评估和改进的更广泛采用.
自动化祖先识别提高了透明度,并确保了科学研究中的适当代表性.

关键词:

人工智能语言模型,开源工具工具.代表祖先的代表性代表性.自动化文本挖掘技术生物医学研究公平性公平性细胞系识别细胞系识别

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