大型语言模型 (LLM) 的现成生物信息能力
bioRxiv : the preprint server for biology
|September 5, 2025
概括
大型语言模型 (LLM) 在生物信息学方面表现有前途,GPT-3.5在基本分析任务上优于Llama-3-70B和GPT-4o. 在各种生物信息学领域中,LLM的表现通常反映了人类的能力.
科学领域:
- 生物信息学和计算生物学
- 生命科学中的人工智能
- 基因组数据分析
背景情况:
- 生物信息学,即对生物序列 (DNA,RNA,蛋白质) 的分析,对于生物研究至关重要.
- 生物信息学的计算性质使其非常适合人工智能驱动的自动化和科学发现的加速.
- 一般用途的大型语言模型 (LLM) 越来越多地在科学学科中探索其潜力.
研究的目的:
- 评估流行的通用LLM的生物信息学能力.
- 在标准化生物信息学问题上比较LLM与人类的表现.
- 确定不同生物信息学领域的LLM的优缺点.
主要方法:
- 我们使用了Rosalind的104个问题,
- 评估了三个LLM:GPT-3.5,Llama-3-70B和GPT-4o.
- 将LLM的表现与全球110至68760名参与者的数据进行比较.
主要成果:
- 其中GPT-3.5获得了最高准确度 (58%),其次是Llama-3-70B (47%) 和GPT-4o (47%).
- 71%的问题至少有一个LLM正确回答.
- 根据类别,LLM的表现不同,在DNA分析方面表现出色,但在序列对齐和基因组组合方面扎,反映了人类的表现趋势.
结论:
- 在生物信息学中,LLM证明了对生物知识,推理,统计分析和代码生成的能力.
- 生物信息学任务中的LLM表现往往与人类表现相关,表明作为科学调查工具的潜力.
- 需要进一步开发LLM以提高其在复杂生物信息应用中的实用性和可靠性.
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