.png){kind=icon}
.png){kind=icon}
Return
Bayesian Graphical Regression
.png){kind=icon}
OA
AI
DOI：10.1080/01621459.2017.1389739.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We consider the problem of modeling conditional independence structures in heterogenous data in the presence of additional subject-level covariatestermed graphical regression. We propose a novel specification of a conditional (in)dependence function of covariateswhich allows the structure of a directed graph to vary flexibly with the covariates; imposes sparsity in both edge and covariate selection; produces both subject-specific and predictive graphs; and is computationally tractable. We provide theoretical justifications of our modeling endeavor, in terms of graphical model selection consistency. We demonstrate the performance of our method through rigorous simulation studies. We illustrate our approach in a cancer genomics-based precision medicine paradigm, where-in we explore gene regulatory networks in multiple myeloma taking prognostic clinical factors into account to obtain both population-level and subject-level gene regulatory networks. Supplementary materials for this article are available online.
Keywords:
Directed acyclic graph
Nonlocal prior
Predictive network
Subject-specific graph
Varying graph structure
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
J
IF:
3
Papers:
5.1K
Citations:
4.8W