对 regmed 和 BayesNetty 的比较,用于探索具有许多变量的因果模型
Richard Howey1, Heather J Cordell1
1Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne, UK.
Genetic epidemiology
|June 27, 2023
概括
监管软件包显示了比BayesNetty更高的精度但更低的回忆力,用于分析生物变量. 缺失的数据显著影响了regmed,但BayesNetty可以将数据归因为改善regmed性能.
科学领域:
- 计算生物学 计算生物学
- 生物信息学是一种生物信息学.
- 统计遗传学 统计遗传学
背景情况:
- 在现代生物研究中,对生物变量之间的复杂因果关系的探索性分析至关重要.
- 现有的用于高维生物数据的因果推理软件包具有不同的优缺点.
研究的目的:
- 为了比较一种新方法的性能,regmed与以前开发的包,BayesNetty,用于分析生物数据中的复杂因果关系.
- 评估高维数据和缺失值对两个软件包性能的影响.
主要方法:
- 对两个软件包进行比较分析:regmed和BayesNetty.
- 评估用于发现因果关系的回忆和精度指标.
- 在高维数据和缺失数据条件下的性能评估.
主要成果:
- 与BayesNetty相比,Regmed表现出明显更高的精度,但回忆能力较低.
- 由于缺少数据,Regmed的表现严重下降,而BayesNetty的表现仅略有下降.
- 贝叶斯Netty对高维数据分析固有的多重测试问题更为敏感.
结论:
- 对于高维度的生物数据,Regmed提供了高精度,但在缺失的值方面存在困难.
- BayesNetty提供了更好的回忆,并更稳定地处理丢失的数据,尽管它更容易受到多重测试问题的影响.
- 在应用 regmed 之前使用 BayesNetty 计算缺失的数据,可以有效地挽救 regmed 在缺失值的数据集中的性能.
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