Conditional Gaussian graphical model for estimating personalized disease symptom networks
Sign Up to like & getrecommendations! Published in 2021 at "Statistics in Medicine"
DOI: 10.1002/sim.9274
Abstract: The co‐occurrence of symptoms may result from the direct interactions between these symptoms and the symptoms can be treated as a system. In addition, subject‐specific risk factors (eg, genetic variants, age) can also exert external… read more here.
Keywords: conditional gaussian; gaussian graphical; symptom networks; graphical model ... See more keywords
Gaussian graphical models with applications to omics analyses
Sign Up to like & getrecommendations! Published in 2022 at "Statistics in Medicine"
DOI: 10.1002/sim.9546
Abstract: Gaussian graphical models (GGMs) provide a framework for modeling conditional dependencies in multivariate data. In this tutorial, we provide an overview of GGM theory and a demonstration of various GGM tools in R. The mathematical… read more here.
Keywords: gaussian graphical; omics analyses; applications omics; models applications ... See more keywords
Bayesian hypothesis testing for Gaussian graphical models: Conditional independence and order constraints
Sign Up to like & getrecommendations! Published in 2020 at "Journal of Mathematical Psychology"
DOI: 10.1016/j.jmp.2020.102441
Abstract: Abstract Gaussian graphical models (GGM; partial correlation networks) have become increasingly popular in the social and behavioral sciences for studying conditional (in)dependencies between variables. In this work, we introduce exploratory and confirmatory Bayesian tests for… read more here.
Keywords: hypothesis; graphical models; conditional independence; gaussian graphical ... See more keywords
Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control
Sign Up to like & getrecommendations! Published in 2022 at "Journal of the American Statistical Association"
DOI: 10.1080/01621459.2022.2044333
Abstract: Transfer learning for high-dimensional Gaussian graphical models (GGMs) is studied with the goal of estimating the target GGM by utilizing the data from similar and related auxiliary studies. The similarity between the target graph and… read more here.
Keywords: gaussian graphical; false discovery; rate; graphical models ... See more keywords
Covariate-adjusted Gaussian graphical model estimation with false discovery rate control
Sign Up to like & getrecommendations! Published in 2020 at "Communications in Statistics - Theory and Methods"
DOI: 10.1080/03610926.2020.1752385
Abstract: Abstract Recent genetic/genomic studies have shown that genetic markers can have potential effects on the dependence structure of genes. Motivated by such findings, we are interested in the estimation of covariate-adjusted Gaussian graphical model (CGGM).… read more here.
Keywords: adjusted gaussian; gaussian graphical; false discovery; graphical model ... See more keywords
The G-Wishart Weighted Proposal Algorithm: Efficient Posterior Computation for Gaussian Graphical Models
Sign Up to like & getrecommendations! Published in 2022 at "Journal of Computational and Graphical Statistics"
DOI: 10.1080/10618600.2022.2050250
Abstract: Gaussian graphical models can capture complex dependency structures amongst variables. For such models, Bayesian inference is attractive as it provides principled ways to incorporate prior information and to quantify uncertainty through the posterior distribution. However,… read more here.
Keywords: gaussian graphical; posterior computation; graphical models; proposal ... See more keywords
GeneNetTools: tests for Gaussian graphical models with shrinkage
Sign Up to like & getrecommendations! Published in 2022 at "Bioinformatics"
DOI: 10.1093/bioinformatics/btac657
Abstract: Abstract Motivation Gaussian graphical models (GGMs) are network representations of random variables (as nodes) and their partial correlations (as edges). GGMs overcome the challenges of high-dimensional data analysis by using shrinkage methodologies. Therefore, they have… read more here.
Keywords: gaussian graphical; tests gaussian; genenettools tests; partial correlations ... See more keywords
Learning of Tree-Structured Gaussian Graphical Models on Distributed Data Under Communication Constraints
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2018.2876325
Abstract: In this paper, learning of tree-structured Gaussian graphical models from distributed data is addressed. In our model, samples are stored in a set of distributed machines where each machine has access to only a subset… read more here.
Keywords: graphical models; communication; learning tree; structured gaussian ... See more keywords
Variational Wishart Approximation for Graphical Model Selection: Monoscale and Multiscale Models
Sign Up to like & getrecommendations! Published in 2019 at "IEEE Transactions on Signal Processing"
DOI: 10.1109/tsp.2019.2953651
Abstract: Graphical models are powerful tools to describe high-dimensional data; they provide a compact graphical representation of the interactions between different variables and such representation enables efficient inference. In particular for Gaussian graphical models, such representation… read more here.
Keywords: graphical models; model; monoscale multiscale; gaussian graphical ... See more keywords
Gibbs Sampling in Inference of Copula Gaussian Graphical Model Adapted to Biological Networks
Sign Up to like & getrecommendations! Published in 2017 at "Acta Physica Polonica A"
DOI: 10.12693/aphyspola.132.1112
Abstract: Markov chain Monte Carlo methods (MCMC) are iterative algorithms that are used in many Bayesian simulation studies, where the inference cannot be easily obtained directly through the defined model. Reversible jump MCMC methods belong to… read more here.
Keywords: sampling inference; gaussian graphical; inference copula; model ... See more keywords
Gaussian Graphical Models Reveal Inter-Modal and Inter-Regional Conditional Dependencies of Brain Alterations in Alzheimer's Disease
Sign Up to like & getrecommendations! Published in 2020 at "Frontiers in Aging Neuroscience"
DOI: 10.3389/fnagi.2020.00099
Abstract: Alzheimer's disease (AD) is characterized by a sequence of pathological changes, which are commonly assessed in vivo using various brain imaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET). Currently, the… read more here.
Keywords: gaussian graphical; alzheimer disease; disease; correlation ... See more keywords