评估基因到表型映射的大型语言模型:全文数据库访问的关键作用
bioRxiv : the preprint server for biology
|July 15, 2025
概括
具有专用数据库的专用大型语言模型 (LLM) 在生物医学研究中的基因对表型映射方面表现出色,提供更高的准确性并支持文献检索.
科学领域:
- 生物医学信息学 生物医学信息学
- 计算生物学 计算生物学
- 人工智能在医学中的应用
背景情况:
- 大型语言模型 (LLM) 在生物学和医学方面表现有前途,但存在不准确性和数据混.
- 这些局限性阻碍了它们用于文学搜索和从科学文本中提取准确信息的使用.
研究的目的:
- 评估LLM在获取同行评审文献和绘制基因与表型相关性方面的表现.
- 为了比较标准的法学士学位,具有数据库的专业法学士学位和没有数据库的专业法学士学位.
主要方法:
- 对198个基因和6个表型的基因对表型映射的评估准确性.
- 包括标准的法学士 (ChatGPT-4o,Gemini 1.5 Pro),具有数据库的专业法学士 (SciSpace,ScholarAI),以及没有数据库的专业法学士 (PubMedGPT,ScholarGPT).
- 使用人类策划的基因与表型映射作为基本真理.
主要成果:
- 专用数据库的专业LLM在基因与表型的映射中实现了80%以上的准确性.
- 这些模型还为每个相关性提供了相关的同行评审出版物.
- 没有数据库的标准LLM和专业LLM的准确性较低.
结论:
- 数据库的增强和专业化显著提高了生物医学研究的LLM可靠性.
- 具有精选数据库的专业LLM对于准确的文献检索和基因现象型相关性分析是优越的.
- 未来的科学LLM开发应该优先考虑数据库集成和领域特异性.
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