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   Articles with "gaussian graphical" as a keyword



Conditional Gaussian graphical model for estimating personalized disease symptom networks

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recommendations! 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

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recommendations! 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

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recommendations! 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

Uncovering critical transitions and molecule mechanisms in disease progressions using Gaussian graphical optimal transport

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recommendations! Published in 2024 at "Communications Biology"

DOI: 10.1038/s42003-025-07995-z

Abstract: Understanding disease progression is crucial for detecting critical transitions and finding trigger molecules, facilitating early diagnosis interventions. However, the high dimensionality of data and the lack of aligned samples across disease stages have posed challenges… read more here.

Keywords: disease; gaussian graphical; graphical optimal; critical transitions ... See more keywords

Cluster Gelnet for estimating Gaussian graphical models with multi-level conditional correlations and block structures

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recommendations! Published in 2025 at "Journal of Statistical Computation and Simulation"

DOI: 10.1080/00949655.2025.2566413

Abstract: This paper introduces a two-step method to exploit hidden block structures in Gaussian graphical models, achieving improved accuracy and scalability with theoretical guarantees and strong empirical performance. This paper addresses the estimation of Gaussian graphical… read more here.

Keywords: cluster gelnet; gaussian graphical; graphical models; block structures ... See more keywords

Transfer Learning in Large-scale Gaussian Graphical Models with False Discovery Rate Control

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recommendations! 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

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recommendations! 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

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recommendations! 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

ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks

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recommendations! Published in 2024 at "Briefings in Bioinformatics"

DOI: 10.1093/bib/bbae610

Abstract: Abstract The Gaussian graphical model (GGM) is a statistical network approach that represents conditional dependencies among components, enabling a comprehensive exploration of disease mechanisms using high-throughput multi-omics data. Analyzing differential and similar structures in biological… read more here.

Keywords: similarity analysis; gaussian graphical; differential similarity; omics networks ... See more keywords

GeneNetTools: tests for Gaussian graphical models with shrinkage

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recommendations! 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

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recommendations! 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