KGG:一个完全自动化的工作流程,用于创建疾病特定的知识图
Reagon Karki1,2, Yojana Gadiya1,2,3, Andrea Zaliani1,2
1Fraunhofer Institute for Translational Medicine and Pharmacology (ITMP), Hamburg, 22525, Germany.
Bioinformatics (Oxford, England)
|June 28, 2025
概括
本研究介绍了知识图生成器 (KGG),这是一个用于构建生命科学知识图的自动化工作流程. KGG加速建立全面的生物和化学实体网络,帮助疾病研究和药物发现.
科学领域:
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
- 计算生物学是一种计算生物学.
背景情况:
- 知识图 (KGs) 对系统生物学至关重要,它使用标准化本体学来表示复杂的生物学和病理生理学数据.
- 手工KG建设是劳动密集型和耗时的,而现有的自动化方法缺乏准确性.
- 在生命科学中需要高效可靠的自动化KG生成.
研究的目的:
- 开发一个自动化工作流来构建生命科学知识图.
- 代表疾病和医疗条件的化学型和表型数据.
- 为了促进复杂的科学查询和下游分析.
主要方法:
- 开发了一个自动化工作流程的知识图表生成器 (KGG).
- 来自策划数据库 (OpenTargets,Uniprot,ChEMBL等) 的综合数据 ) 的情况.
- 采用了一个类似于时钟机制的数据集成模式.
主要成果:
- 产生了疾病相关实体 (蛋白质,途径,化学物质等) 的全面KG. ) 的情况.
- 确定了用于共患病分析的共享实体,并将KG与外部来源进行了比较.
- 在帕金森病中证明了用于目标识别和药物重定向的用例.
结论:
- 在生命科学领域,KGG提供了一种自动化和高效的KG构造方法.
- 生成的KG支持各种应用,包括并发症分析和药物发现.
- 工作流允许探索药物相似性和蛋白质结构识别.
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