在民族药理学中重新思考网络分析:一个多学科和人工智能路线图,以克服概念和方法方面的偏见
Xuewen Diao1,2, Hao Zhang3, Shiqi Wang4
1The First Affiliated Hospital of Henan University of Chinese Medicine Department of Andrology, Zhengzhou, China.
Frontiers in pharmacology
|March 9, 2026
概括
在民族药理学中,网络分析存在同质性问题,重复识别相同的分子. 整合经验数据和人工智能的新框架可以提高药物发现的多样性和相关性.
科学领域:
- 计算生物学是一种计算生物学.
- 药理学 药理学 是一个学科.
- 民族植物学 民族植物学
背景情况:
- 网络分析 (NA) 对民族药理学至关重要,可以预测目标和假设.
- 一个关键的局限性是,在各种研究中,经常识别一组狭窄的分子 (例如氨酸).
- 这种同质性源于数据库偏差,被分析方法放大,创造了一个"融合发现管道".
研究的目的:
- 系统地分析基于网络的民族药理学研究中的"同质性"模式.
- 概念化驱动这种同质性的机制.
- 提出一个新的框架,以增强在民族药理学中NA的多样性和相关性.
主要方法:
- 对1038个基于网络的民族药理学研究进行了系统分析.
- 在多个层面上识别和描述"同质性"模式 (黄类中心性,枢纽-目标核心,正规路径).
- 评估缓解同质性的方法,包括整合上下文实验和多omics数据.
主要成果:
- 建立了一个多层次的"同质性"模式,其特点是"黄类中心性"",枢纽-目标核心"和受限制的"法典途径".
- 这种模式代表了由数据库偏差和不敏感的分析方法驱动的自我强化的"融合发现管道".
- 综合上下文实验或多omics数据被证明可以有效地缓解同质性.
结论:
- 在NA中普遍存在的同质性阻碍了民族药理学研究的预测有效性和生物相关性.
- 需要从数据库依赖转向经验驱动的数据采集.
- 使用具有偏见意识的AI和动态网络建模的综合框架可以促进更强大,多样化和临床相关的民族药理学发现.
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