Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions

2014 ◽  
pp. 121-146
Author(s):  
Marine Jeanmougin ◽  
Camille Charbonnier ◽  
Mickaël Guedj ◽  
Julien Chiquet
Keyword(s):  
Breast Cancer ◽  
Graphical Models ◽  
Network Inference ◽  
Gaussian Graphical Models

Gaussian Graphical Models Identified Food Intake Networks among Iranian Women with and without Breast Cancer: A Case-Control Study

2020 ◽  
pp. 1-8
Author(s):  
Samira Sadat Fereidani ◽  
Fatemeh Sedaghat ◽  
Hassan Eini-Zinab ◽  
Zeinab Heidari ◽  
Saba Jalali ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Food Intake ◽  
Graphical Models ◽  
Case Control Study ◽  
Case Control ◽  
Gaussian Graphical Models ◽  
Iranian Women ◽  
Control Study

scholarly journals Systematic evaluation of normalization methods for glycomics data based on performance of network inference

2019 ◽  
Author(s):  
Elisa Benedetti ◽  
Nathalie Gerstner ◽  
Maja Pučić-Baković ◽  
Toma Keser ◽  
Karli R. Reiding ◽  
...  
Keyword(s):  
Graphical Models ◽  
Large Scale ◽  
Network Inference ◽  
Systematic Evaluation ◽  
Gaussian Graphical Models ◽  
Data Types ◽  
Experimental Conditions ◽  
Normalization Methods ◽  
Glycosylation Pathway

AbstractGlycomics measurements, like all other high-throughput technologies, are subject to technical variation due to fluctuations in the experimental conditions. The removal of this non-biological signal from the data is referred to as normalization. Contrary to other omics data types, a systematic evaluation of normalization options for glycomics data has not been published so far. In this paper, we assess the quality of different normalization strategies for glycomics data with an innovative approach. It has been shown previously that Gaussian Graphical Models (GGMs) inferred from glycomics data are able to identify enzymatic steps in the glycan synthesis pathways in a data-driven fashion. Based on this finding, we here quantify the quality of a given normalization method according to how well a GGM inferred from the respective normalized data reconstructs known synthesis reactions in the glycosylation pathway. The method therefore exploits a biological measure of goodness. We analyzed 23 different normalization combinations applied to six large-scale glycomics cohorts across three experimental platforms (LC-ESI-MS, UHPLC-FLD and MALDI-FTICR-MS). Based on our results, we recommend normalizing glycan data using the ‘Probabilistic Quotient’ method followed by log-transformation, irrespective of the measurement platform.

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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