Collaborative Research: Bayesian Network Estimation across Multiple Sample Groups and Data Types

作为这项合作研究的一部分，研究人员将开发新的统计方法来估计多个图形网络。该研究将解决学习网络的挑战，当被考虑的对象和变量之间存在异质性时，在图形建模和贝叶斯统计方面开辟了新的领域。所开发的方法将对统计和自然产生网络估计问题的应用领域产生重大影响。特别是，应用在神经影像学将探讨。该项目将包括对研究生的教育和培训活动。研究结果将传播给研究界，并用于进一步的跨学科合作努力。软件和代码将开发和存储在公共存储库。当所有样本都是在相似的条件下收集或反映单一类型的疾病时，可以使用图形套索或贝叶斯网络推理方法来学习潜在的条件依赖关系。然而，在许多研究中，在不同的实验环境或其他异质性条件下获得不同亚型或疾病的样本。当考虑多种数据类型时，挑战变得更加艰巨。该项目将侧重于开发贝叶斯方法，以学习跨多个样本组的单一数据类型的网络，该方法既可以将组间的边值链接起来，也可以灵活地建模哪些组最相似。方法还将扩展到从异构主题集和异构数据类型的网络的分层建模框架。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

As part of this collaborative research, the investigators will develop new statistical methods for the estimation of multiple graphical networks. The proposed research will address the challenge of learning networks when there is heterogeneity among both the subjects and the variables considered, breaking new ground in graphical modeling and Bayesian statistics. The methods developed will have the potential for significant impact in statistics and in applied fields in which problems of network estimation naturally arise. In particular, applications in neuroimaging will be explored. The project will include educational and training activities for graduate students. Findings will be disseminated to the research community and used to further interdisciplinary collaborative efforts. Software and code will be developed and deposited in public repositories.When all samples are collected under similar conditions or reflect a single type of disease, methods such as the graphical lasso or Bayesian network inference approaches can be applied to learn the underlying conditional dependence relations. In many studies, however, samples are obtained for different subtypes or disease, under varying experimental settings, or other heterogeneous conditions. The challenge becomes even more formidable when multiple data types are under consideration. This project will focus on the development of Bayesian methods to learn networks for a single data type across multiple sample groups using an approach that both links edge values across groups, and flexibly models which groups are most similar. Methods will also be extended to a hierarchical modeling framework of networks from both heterogeneous sets of subjects and heterogeneous data types.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.