scholarly journals Feedback Message Passing for Inference in Gaussian Graphical Models

2012 ◽  
Vol 60 (8) ◽  
pp. 4135-4150 ◽  
Author(s):  
Ying Liu ◽  
Venkat Chandrasekaran ◽  
Animashree Anandkumar ◽  
Alan S. Willsky
Keyword(s):  
Graphical Models ◽  
Message Passing ◽  
Gaussian Graphical Models ◽  
Feedback Message

scholarly journals Fast Bayesian inference in large Gaussian graphical models

Biometrics ◽  
2019 ◽  
Vol 75 (4) ◽  
pp. 1288-1298
Author(s):  
Gwenaël G. R. Leday ◽  
Sylvia Richardson
Keyword(s):  
Bayesian Inference ◽  
Graphical Models ◽  
Gaussian Graphical Models

scholarly journals Gaussian graphical modeling of the serum exposome and metabolome reveals interactions between environmental chemicals and endogenous metabolites

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Vincent Bessonneau ◽  
Roy R. Gerona ◽  
Jessica Trowbridge ◽  
Rachel Grashow ◽  
Thomas Lin ◽  
...  
Keyword(s):  
Graphical Models ◽  
Association Studies ◽  
Environmental Chemicals ◽  
Gaussian Graphical Models ◽  
Metabolomics Data ◽  
Health And Nutrition ◽  
Novel Approach ◽  
Disease Causation ◽  
Women Firefighters ◽  
Endogenous Metabolites

AbstractGiven the complex exposures from both exogenous and endogenous sources that an individual experiences during life, exposome-wide association studies that interrogate levels of small molecules in biospecimens have been proposed for discovering causes of chronic diseases. We conducted a study to explore associations between environmental chemicals and endogenous molecules using Gaussian graphical models (GGMs) of non-targeted metabolomics data measured in a cohort of California women firefighters and office workers. GGMs revealed many exposure-metabolite associations, including that exposures to mono-hydroxyisononyl phthalate, ethyl paraben and 4-ethylbenzoic acid were associated with metabolites involved in steroid hormone biosynthesis, and perfluoroalkyl substances were linked to bile acids—hormones that regulate cholesterol and glucose metabolism—and inflammatory signaling molecules. Some hypotheses generated from these findings were confirmed by analysis of data from the National Health and Nutrition Examination Survey. Taken together, our findings demonstrate a novel approach to discovering associations between chemical exposures and biological processes of potential relevance for disease causation.

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