Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.

图形模型是功能强大的工具，通常用于研究高通量生物医学数据集中的复杂依赖性结构。它们允许对各种生物过程进行整体的系统级视图，以进行直观而严格的理解和解释。在大型网络中，贝叶斯方法特别适合，因为它会鼓励图的稀疏性，合并先验信息，并且最重要的是要考虑图结构的不确定性。这些功能在样本量有限的应用中尤其重要，包括基因组学和成像研究。在本文中，我们回顾了几种最近开发的用于在非标准设置下分析大型网络的技术，包括但不限于：从多个相关子组观察到的数据的多个图表，用于分析随协变量而变化的网络的图形回归方法以及其他复杂的采样和结构设置。我们还使用癌症基因组学和神经影像学中的实例说明了其中一些方法的实际实用性。