textToKnowledgeGraph:生成分子相互作用知识图,使用大型语言模型在细胞景观中进行探索
Favour James1, Christopher Churas2, Dexter Pratt2
1Department of Electronic and Electrical Engineering, Obafemi Awolowo University.
bioRxiv : the preprint server for biology
|August 8, 2025
概括
本研究介绍了textToKnowledgeGraph,这是一种利用大型语言模型 (LLM) 来自动从科学文献中提取生物关系到生物表达语言 (BEL) 格式的AI工具.
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
- 自然语言处理自然语言处理.
背景情况:
- 知识图 (KG) 对于生物数据分析至关重要,但从文献中手动构建是资源密集的.
- 现有的文本挖掘工具与上下文理解和推断复杂的生物关系作斗争.
- 大型语言模型 (LLM) 为更准确的信息提取提供了增强的上下文知识.
研究的目的:
- 开发一种自动化方法,从非结构化文本中提取生物相互作用.
- 在结构化生物表达语言 (BEL) 格式中表示提取的关系.
- 克服传统文本挖掘方法在捕捉复杂的生物背景方面的局限性.
主要方法:
- 利用大型语言模型 (LLM) 进行高级文本理解和关系提取.
- 开发了开源的Python包textToKnowledgeGraph用于自动化的BEL提取.
- 在Cytoscape Web中集成了一个交互式应用程序,以简化提取和探索.
主要成果:
- 从科学文章中成功地将生物相互作用提取到 BEL 格式.
- 创建了一个开源工具和一个用于知识图表构建的交互式应用程序.
- 创建了一个经过审查的数据集,以促进未来自动化生物知识提取方面的进步.
结论:
- 基于LLM的 textToKnowledgeGraph有效地自动化了生物关系的提取.
- 开发的工具和数据集降低了创建结构化生物知识图的障碍.
- 这种方法提高了生物文学对计算分析的可访问性和实用性.
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