使用先验知识数据库对omics数据的因果网络分析
1Biomedical Data Science Group, Luxembourg Centre for Systems Biomedicine (LCSB), University of Luxembourg, 7, avenue des Hauts Fourneaux, L-4362 Esch-sur-Alzette, Luxembourg.
Briefings in bioinformatics
|December 5, 2025
概括
本综述对数据驱动的因果奥米克分析方法进行了分类,整合了生物洞察的先前知识. 它指导研究人员选择适当的方法来识别复杂分子网络中的因果关系.
科学领域:
- 基因组学就是基因组学.
- 系统生物学 系统生物学
- 生物信息学是一种生物信息学.
背景情况:
- 识别omics数据中的因果关系对于理解生物过程至关重要.
- 挑战包括复杂的分子网络和观察数据的局限性.
- 来自数据库的结构化先验知识是准确的因果推理的关键.
研究的目的:
- 系统地审查数据驱动的因果奥米克分析方法,整合先前的知识.
- 根据先前的知识整合水平对这些方法进行分类.
- 为研究人员提供关于选择和应用因果分析方法的实用指南.
主要方法:
- 对因果奥米克分析方法的系统文献综述.
- 将方法分为规则级,流程级和网络级的方法.
- 分析方法的优点,局限性和应用.
主要成果:
- 确定了三个主要方法:规则级,流量级和网络级.
- 每种方法在解释,范围和复杂性方面都提供了不同的权衡.
- 方法在癌症,脏疾病和神经退行性疾病等多种应用中显示出实用性.
结论:
- 以先前知识为指导的因果关系分析方法对于生物发现至关重要.
- 方法的选择取决于研究问题,数据和所需的详细程度.
- 需要进一步的研究来解决局限性,并加强omics数据中的因果推理.
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