贝叶斯动态网络建模:用于心血管疾病中的代谢关联的应用
Marco Molinari1, Andrea Cremaschi2, Maria De Iorio1,2,3
1Department of Statistical Science, University College, London, London, UK.
Journal of applied statistics
|January 5, 2024
概括
这项研究引入了一种新的贝叶斯方法来分析代谢物关联如何随着时间的推移而变化,以及种族之间的差异. 该方法通过检查SABRE研究中的代谢物数据来帮助理解心脏代谢障碍风险.
科学领域:
- 代谢学 代谢学 代谢学
- 统计遗传学 统计遗传学
- 计算生物学 计算生物学
背景情况:
- 心脏代谢障碍在不同种族群体中存在不同的风险.
- 了解随着时间的推移代谢物协会的种族差异对于公共卫生至关重要.
- 萨索尔和布伦特REVISITED (SABRE) 研究为这项研究提供了有价值的数据集.
研究的目的:
- 开发一种新的贝叶斯方法来估计多个图形模型.
- 分析不同族群 (欧洲人和南亚人) 的代谢物关联的时间模式.
- 识别代谢物水平及其随时间的关联中的种族特异性差异.
主要方法:
- 在贝叶斯框架内采用节点回归方法.
- 使用动态马在对图形结构推理回归系数施加稀疏性之前.
- 估计两个时间点测量的代谢物水平的高维精度矩阵.
主要成果:
- 拟议的方法允许估计反映代谢物关联的稀疏图形模型.
- 该框架可以比较跨族群和时间点的代谢物网络结构.
- 提供了使用Stan (哈密尔顿蒙特卡洛) 和块吉布斯采样方案来拟合模型的代码.
结论:
- 新的贝叶斯图形模型方法有效地分析了跨种族的时间代谢物协会.
- 这种方法可以揭示特定种族对心脏代谢障碍风险因素的见解.
- 该研究为分析队列研究中复杂的生物数据提供了灵活的框架.
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