在生物医学文献中,即时工程对蛋白质-蛋白质相互作用识别的大型语言模型的影响
Yung-Chun Chang1,2, Ming-Siang Huang3, Yi-Hsuan Huang3
1Graduate Institute of Data Science, Taipei Medical University, Taipei, Taiwan. changyc@tmu.edu.tw.
Scientific reports
|May 3, 2025
概括
一般领域的大型语言模型 (LLM) 显示了蛋白质-蛋白质相互作用 (PPI) 提取的前景. 谷歌双子和GPT-4展示了有效的PPI预测与战略提示,帮助研究人员没有计算专业知识.
科学领域:
- 生物医学自然语言处理
- 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:
- 蛋白与蛋白相互作用 (PPI) 对于理解生物过程至关重要.
- 有专门的计算模型用于PPI提取,但它们的使用需要大量的专业知识.
- 一般域的大型语言模型 (LLM) 为更广泛的可访问性提供了潜在的替代方案.
研究的目的:
- 评估一般领域的LLM (GPT-3.5,GPT-4,Google Gemini) 在蛋白质与蛋白质相互作用 (PPI) 预测方面的有效性.
- 探索系统性提示工程对PPI提取中的LLM性能的影响.
- 评估LLMs对缺乏专业计算技能的研究人员的实用性.
主要方法:
- 系统的提示工程与六种情景越来越复杂.
- 在多个基准数据集 (LLL,IEPA,HPRD50,AIMed,BioInfer,PEDD) 上对专有LLM (GPT-3.5,GPT-4,Google Gemini) 的评估.
- 分析模型性能,包括识别和减轻预测偏差.
主要成果:
- 双子1.5 Pro在大多数数据集中实现了最高的性能,具有显着的F1得分 (例如,LLL中的90.3%).
- GPT-4表现出竞争力的表现,特别是在LLL数据集 (87.3%的F1得分).
- 快速工程显著改善了PPI预测中的LLM性能,并成功解决了偏差.
结论:
- 一般目的的LLM,当被精心设计的提示指导时,可以有效地进行蛋白质-蛋白质相互作用预测.
- 这些LLM为生物医学研究人员提供了有价值的工具,降低了计算分析的障碍.
- 虽然不超过专业模型,但LLM为可访问的PPI提取提供了实用解决方案.
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