集群药用植物,分子,疾病和目标的知识图集成
U K Shajil1, Jaleel Uca2, S Sathish2
1Department of Bioscience and Engineering, National Institute of Technology Calicut, Kerala 673601, India.
Computational biology and chemistry
|January 10, 2026
概括
药用植物中的植物化学物质形成集群,具有独特的效果. 这项研究将这些集群映射到疾病中,揭示了传统疗法的网络级药理学.
科学领域:
- 植物化学和系统药理学 系统药理学
- 化学信息学和网络生物学
背景情况:
- 植物化学物质单独表现出不同的特性,但在分子中可以表现出协同效应.
- 传统的多草药配方,如Dasamoola,含有复杂的植物化学物质混合物,具有潜在的网络药理学.
研究的目的:
- 分析Dasamoola的植物化学集群,并将其映射到疾病状况.
- 开发一种统一的工作流程,用于分析多草药配方,使用聚类,ICD-11映射和知识图.
- 探索系统级药理学,并为传统医学提出假设.
主要方法:
- 对来自十种药用植物的490种植物成分进行了系统分析.
- 化学信息集群 (K-Means) 和缩小维度 (t-SNE) 用于将分子组织成49个集群.
- 将分子集群映射到87个ICD-11疾病条件中,并使用Neo4j知识图与疾病本体学集成.
主要成果:
- 确定了49个结构连贯的植物化学集群.
- 通过热图分析,通过分子和疾病表型之间发现了显著的相关性.
- 可视化分子-点-疾病关联,建议网络级药理学机制.
结论:
- 结构相关的植物化学物质通过共同的生物点与特定的疾病类别有关.
- 开发的工作流提供了一个模板来分析其他多草药配方.
- 这些发现为理解系统药理学和产生实验假设提供了新的途径.
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