DD-CC-II:数据驱动的细胞与细胞相互作用推断及其对COVID-19的应用
Heewon Park1,2,3, Satoru Miyano2,3
1School of Mathematics Statistics and Data Science, Sungshin Women's University, Seoul 01133, Republic of Korea.
International journal of molecular sciences
|October 29, 2025
概括
一个新的计算框架,数据驱动的细胞-细胞相互作用推断 (DD-CC-II),准确地模拟细胞-细胞相互作用. 这种方法揭示了疾病特定的沟通模式,改善了我们对组织平衡和疾病进展的理解.
科学领域:
- 计算生物学是一种计算生物学.
- 系统生物学 系统生物学
- 基因组学就是基因组学.
背景情况:
- 细胞与细胞的相互作用对于组织平衡和疾病至关重要.
- 现有的联体受体数据库在与特定上下文信号进行斗争.
研究的目的:
- 引入一个新的数据驱动计算框架 (DD-CC-II) 来推断细胞与细胞相互作用 (CCI).
- 克服传统方法在捕获动态和特定环境信号方面的局限性.
主要方法:
- 开发了一个基于图形的模型,使用"eigen-cells"来表示细胞组.
- 构建相关系数网络以建模自身细胞之间的关联.
- 使用过度表示分析和超几何测试验证的统计学意义.
主要成果:
- 通过蒙特卡洛模拟,DD-CC-II在推断CCI方面表现出优异的性能,与通过蒙特卡洛模拟的基于联结体受体的方法相比.
- 对COVID-19数据的分析确定了严重程度特定的相互作用模式.
- 特定的标记物 (高严重程度的FOS,CXCL8,HLA-A;低严重程度的IL1B,CD3D,CCL5) 和全身性红斑狼途径与疾病严重程度有关.
结论:
- DD-CC-II提供了一个以数据为中心的方法来绘制蜂通信的地图.
- 这一框架增强了对细胞间水平疾病进展的理解.
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