使用大型语言模型 (LLM) 进行微生物组样本来源的增强语义分类
Daniela Gaio1, Janko Tackmann1, Eugenio Perez-Molphe-Montoya1
1Department of Molecular Life Sciences and Swiss Institute of Bioinformatics, University of Zürich, Zürich, CH-8057, Switzerland.
GigaScience
|February 12, 2026
概括
大型语言模型 (LLM) 可以经济有效地自动化环境测序数据的重新注释,提高其公平性和AI准备性. 开放权重的LLM显示了与专有模型对生态元数据重新注释的准确性相似的准确性.
科学领域:
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
- 环境科学 环境科学
背景情况:
- 中央序列存储库已经大幅增长,但异质元数据质量阻碍了数据的重用.
- 对下游研究,特别是微生物组研究中,注释良好的测序数据至关重要.
- 提高数据的公平性 (可查找性,可访问性,互操作性,可重复使用性) 和人工智能准备性对于复杂的生物元数据至关重要.
研究的目的:
- 评估大型语言模型 (LLM) 的成本效益和性能,以自动化环境测序记录的重新注释.
- 评估LLM的潜力,以提高微生物组测序元数据的公平性和AI准备性,而无需微调.
- 为了比较专有LLM与生态元数据重新注释的开放权重替代品的性能.
主要方法:
- 利用OpenAI生成预训练变压器 (GPT) 模型,根据简化生态环境分类方案自动重新注释序列记录.
- 开发并测试了一条用于重新注释环境测序元数据的管道,重点关注可扩展性,时间和成本效益.
- 将LLM性能与手动策划的,非机器学习 (ML) 基于关键字的基线进行比较,并评估专有与开放权重模型 (Qwen,meta-Llama,microsoft-phi-4).
主要成果:
- 基于LLM的注释显著超过了基线关键字方法.
- 发现快速设计对于任务匹配至关重要,而模型选择对性能影响较小.
- 开放式重量LLM表现出与专有模型对生物群和亚生物群分类的准确性相比较.
- 优化管道应用于200万个环境测序记录,产生标准化的全球样本来源注释.
结论:
- 从环境测序记录中,LLM提供了一种具有成本效益的解决方案,用于自动化标准化和简化来自环境测序记录的复杂生物元数据.
- 该研究验证了LLM的使用,以提高大规模测序数据集的公平性和AI准备性.
- 开放式重量LLM为生态元数据重新注释提供了可行且准确的替代方案,促进了更广泛的可访问性和应用.
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