根据样本特定因果关系网络 (SCNE) 发现疾病进展期间的恶化前状态
Jiayuan Zhong1, Hui Tang1, Ziyi Huang2
1School of Mathematics and Big Data, Foshan University, Foshan 528000, China.
Research (Washington, D.C.)
|May 8, 2024
概括
检测复杂疾病中的关键状态对于防止恶化至关重要. 一种新的方法,样本特定因果关系网络 (SCNE),使用高维数据准确识别这些预恶化状态.
科学领域:
- 生物医学数据分析
- 复杂系统生物学 复杂系统生物学
- 网络医学 网络医学
背景情况:
- 复杂的疾病可能会出现突然的转变,或临界点,表明危急状态.
- 早期发现预恶化状态对于管理严重疾病进展至关重要.
- 传统的统计方法在复杂疾病中常见的高维,有限样本数据方面扎.
研究的目的:
- 引入一种新的定量方法,即样本特定因果关系网络 (SCNE),用于检测复杂疾病中的关键状态.
- 解决现有方法在识别高维生物数据中的预恶化状态方面的局限性.
- 捕捉分子因果关系中的动态变化,以确定疾病的临界点.
主要方法:
- 推断个体受试者的特定样本因果关系网络.
- 分子因果关系中的量化动态变化.
- 将SCNE应用于数值模拟和现实数据集 (结肠直肠癌,流感,TCGA瘤).
主要成果:
- 在复杂疾病中,SCNE准确地确定了临界点和预恶化状态.
- 该方法与现有的六种单样方法相比,表现优越.
- 通过模拟和各种生物数据集验证的发现,包括单细胞和癌症基因组学数据.
结论:
- 在复杂疾病中,SCNE是检测危急状态和预恶化的有效工具.
- 该方法为分析高维生物数据提供了强大的解决方案.
- 计算发现进一步得到了信号生物标志物的分析的支持.
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