Fast hybrid Bayesian integrative learning of multiple gene regulatory networks for type 1 diabetes

Time-varying dynamic Bayesian network learning for an fMRI study of emotion processing

This article presents a novel method for learning time-varying dynamic Bayesian networks. The proposed method breaks down the dynamic Bayesian network learning problem into a sequence of regression inference problems and tackles each problem using the Markov neighborhood regression technique. Notably, the method demonstrates scalability concerning data dimensionality, accommodates time-varying network structure, and naturally handles multi-subject data. The proposed method exhibits consistency and offers superior performance compared to existing methods in terms of estimation accuracy and computational efficiency, as supported by extensive numerical experiments. To showcase its effectiveness, we apply the proposed method to an fMRI study investigating the effective connectivity among various regions of interest (ROIs) during an emotion-processing task. Our findings reveal the pivotal role of the subcortical-cerebellum in emotion processing.

Joint estimation of multiple mixed graphical models for pan-cancer network analysis

Graphical models have been used in many scientific fields for exploration of conditional independence relationships for a large set of random variables. Although a variety of methods have been proposed in the literature for estimating graphical models with different types of data, none of them is applicable for jointly estimating multiple mixed graphical models. To tackle this problem, we propose a joint mixed learning method. The proposed method is very flexible, which works for various mixed types of data, such as those mixed with Gaussian, multinomial, and Poisson, and also allows people to incorporate domain knowledge into network construction by restricting some links to be included in or excluded from the networks. As an application, the proposed method is applied to pan-cancer network analysis for six types of cancer with data from The Cancer Genome Atlas. To our knowledge, this is the first work for joint estimation of multiple mixed graphical models.