米特征本体学的策划系统,通过LLM和PubAnnotation可靠的互操作
Javeed Muhammad Ahmad1, Yawen Liu1, Jin-Dong Kim2
1College of Informatics, Hubei Key Laboratory of Agricultural Bioinformatics, Huazhong Agricultural University, Wuhan, China.
Genomics & informatics
|December 2, 2025
概括
这项研究引入了一种使用大型语言模型 (LLM) 和数据库的自动化系统,用于策划米特征本体学,显著提高了农业基因组学手动方法的效率和数据准确性.
科学领域:
- 农业基因组学 农业基因组学
- 生物信息学是一种生物信息学.
- 计算生物学 计算生物学
背景情况:
- 本体学框架对于组织复杂的生物数据,如基因和表型至关重要.
- 用于本体论注释的手册文献审查是耗时且难以扩展的.
- 大型语言模型 (LLM) 和策划数据库为半自动化的本体学策划提供了潜力.
研究的目的:
- 为米特征本体学开发一个高效的基于文献的策划系统.
- 将LLMs与现有数据库集成为半自动化的本体学策划.
- 为了比较自动化系统与手动策划方法的性能.
主要方法:
- 开发了一个策划系统,整合了Rice-Alterome和PubAnnotation.
- 使用LLM (DeepSeek,KIMI) 通过API进行自动信息提取和注释.
- 采用快速工程来实现LLM集成,并通过使用案例评估系统.
主要成果:
- 与手动方法相比,该系统大大改善了文献证据的检索和组织.
- 综合平台简化了本体学策划和证据发现.
- 在LLM驱动的查询中,识别了在手动策划中忽略的隐含或缺失的信息.
结论:
- 将LLMs与Rice-Alterome和PubAnnotation集成,为自动化米特征本体学策划提供了一个有希望的解决方案.
- 这种方法加快了证据收集,提高了数据的一致性和可访问性.
- 未来的工作将把框架扩展到其他作物,如小麦和玉米.
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