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.
