Inferring metabolic networks using the Bayesian adaptive graphical Lasso with informative priors

DOI10.4310/SII.2013.v6.n4.a12zbMath1326.92028OpenAlexW2142063839WikidataQ37579999 ScholiaQ37579999MaRDI QIDQ896597

Marina Vannucci, Mirjana Maletic-Savatic, Cemal Karakas, William Choi, Christine Peterson, Li-hua Ma

Publication date: 10 December 2015

Published in: Statistics and Its Interface (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.4310/sii.2013.v6.n4.a12

zbMATH Keywords

graphical modelsmetabolic networkinformative priorneuroinflammationBayesian adaptive graphical Lasso

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Bayesian inference (62F15) Graphical methods in statistics (62A09) Systems biology, networks (92C42)

Related Items (8)

Survival prediction and variable selection with simultaneous shrinkage and grouping priors ⋮ Bayesian analysis of nonparanormal graphical models using rank-likelihood ⋮ Incorporating grouping information into Bayesian Gaussian graphical model selection ⋮ Using Bayesian latent Gaussian graphical models to infer symptom associations in verbal autopsies ⋮ Bayesian nonlinear model selection for gene regulatory networks ⋮ Post-processing posteriors over precision matrices to produce sparse graph estimates ⋮ Hierarchical normalized completely random measures for robust graphical modeling ⋮ An Expectation Conditional Maximization Approach for Gaussian Graphical Models

Uses Software

• Bioconductor
• KEGGgraph
• igraph
• KEGG

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