植物Connectome:一个知识图数据库,包含超过71,000个植物文章
Shan Chun Lim1, Manoj Itharajula1, Mads Harder Møller2
1School of Biological Sciences, Nanyang Technological University, 60 Nanyang Drive, Singapore 637551, Singapore.
The Plant cell
|July 23, 2025
概括
研究人员使用人工智能 (AI) 分析了植物生物学文献,发现了数以百万计的基因和代谢物相互作用. 这种人工智能驱动的方法有助于科学家追踪植物科学中的功能关系.
科学领域:
- 植物生物学 植物生物学
- 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:
- 了解基因代谢物网络对于植物发育和环境反应至关重要.
- 越来越多的科学文献对保持对功能遗传学的最新了解提出了挑战.
- 文本挖掘和大型语言模型 (LLM) 为文学分析提供了潜在的解决方案.
研究的目的:
- 为了利用LLMs进行植物生物学摘要的文本挖掘,以确定功能关系.
- 创建一个关于生物实体及其相互作用的全面数据库.
- 帮助植物科学家在广的科学文献中进行导航.
主要方法:
- 使用LLM文本挖掘功能处理了超过71,000份植物生物学摘要.
- 识别和编目了生物实体 (基因,代谢物等) 之间的功能关系. ) 的情况.
- 开发了PlantConnectome数据库来存储和呈现提取的信息.
主要成果:
- 在240万个生物实体之间提取了近500万个功能关系.
- 在识别这些关系方面取得了超过85%的高准确率.
- 证明了数据库在分析基因调节网络,蛋白质-蛋白质相互作用和应激反应方面的实用性.
结论:
- 人工智能驱动的文本挖掘可以有效处理大量的科学文献.
- 植物Connectome数据库为植物功能网络提供了有价值的见解.
- 这种方法有助于保持最新的植物生物学研究的进展.
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