Bayesian Edge Regression in Undirected Graphical Models to Characterize Interpatient Heterogeneity in Cancer
DOI10.1080/01621459.2021.2000866OpenAlexW3214603741MaRDI QIDQ5885074
Wen-Yi Wang, Jeffrey S. Morris, Unnamed Author, Veerabhadran Baladandayuthapani, Unnamed Author, Unnamed Author, Unnamed Author
Publication date: 27 March 2023
Published in: Journal of the American Statistical Association (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/2101.09587
undirected graphical modelsgene regulatory networktumor heterogeneityBayesian adaptive shrinkagenonstatic graph
Ridge regression; shrinkage estimators (Lasso) (62J07) Applications of statistics to biology and medical sciences; meta analysis (62P10) Learning and adaptive systems in artificial intelligence (68T05) Probabilistic graphical models (62H22)
Uses Software
Cites Work
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- Structural similarity and difference testing on multiple sparse Gaussian graphical models
- Bayesian variable selection with shrinking and diffusing priors
- Joint estimation of precision matrices in heterogeneous populations
- Bayesian graphical models for differential pathways
- Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem
- Bayesian inference for multiple Gaussian graphical models with application to metabolic association networks
- Needles and straw in haystacks: Empirical Bayes estimates of possibly sparse sequences
- Needles and straw in a haystack: posterior concentration for possibly sparse sequences
- Time varying undirected graphs
- High-dimensional graphs and variable selection with the Lasso
- Gene network reconstruction using global-local shrinkage priors
- Inference with normal-gamma prior distributions in regression problems
- Joint estimation of multiple high-dimensional precision matrices
- BAYESIAN HYPER-LASSOS WITH NON-CONVEX PENALIZATION
- A Covariance Regression Model
- Joint estimation of multiple graphical models
- A sparse ising model with covariates
- Bayesian Analysis of Mass Spectrometry Proteomic Data Using Wavelet-Based Functional Mixed Models
- The horseshoe estimator for sparse signals
- The Bayesian Lasso
- Bayesian Variable Selection in Linear Regression
- Simultaneous Clustering and Estimation of Heterogeneous Graphical Models
- Modeling Protein Expression and Protein Signaling Pathways
- Bayesian Structure Learning in Multilayered Genomic Networks
- The Joint Graphical Lasso for Inverse Covariance Estimation Across Multiple Classes
- Testing differential networks with applications to the detection of gene-gene interactions
- Bayesian Inference of Multiple Gaussian Graphical Models
- Dirichlet–Laplace Priors for Optimal Shrinkage
- Covariate-adjusted precision matrix estimation with an application in genetical genomics
- On Joint Estimation of Gaussian Graphical Models for Spatial and Temporal Data
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