ArticleStatistics in medicine2022
Gaussian graphical models with applications to omics analyses.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed, 24 citations in OpenAlex.
- Laser Therapy Effects on Periodontal Status: A Randomized Study Using Gaussian Network Analysis and Structural Equation Modeling Approach.Medicina (Kaunas, Lithuania) · 2024Trial
- Gender differences in the association between anxiety and behavioral problems in 31,400 Chinese children: a cross-sectional regression and network analysis.BMC psychiatry · 2026Article
- A network analysis of lifetime stressor exposure, mental health, well-being, and immune cell mobilisation to acute stressors in young adults.Brain, behavior, & immunity - health · 2026Article
- Analysis of comorbidity patterns of pregnancy and delivery complications in the female population.Frontiers in global women's health · 2026Article
- A network-guided penalized regression with application to proteomics data.Bioinformatics advances · 2026Article
- tvsfglasso: Time-varying scale-free graphical lasso for network estimation from time-series data.PLoS computational biology · 2025Article
- Subgroup Analysis of Differential Networks with Latent Variables.Statistics and computing · 2025Article
- Network Approach to Evaluate the Effect of Diet on Stroke or Myocardial Infarction Using Gaussian Graphical Model.Nutrients · 2025Article
- The Association of Circulating Immune Cells With Cognitive Function, Brain Imaging, and Incident All-cause and Alzheimer's Dementia: The Framingham Offspring Study.The journals of gerontology. Series A, Biological sciences and medical sciences · 2025Article
- Machine learning and network analysis with focus on the biofilm inComputational and structural biotechnology journal · 2024Article
- ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.Briefings in bioinformatics · 2024Article
- Inflammatory protein associations with brain MRI measures: Framingham Offspring Cohort.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- Spatial Cellular Networks from omics data with SpaCeNet.Genome research · 2024Article
- Article
- SEMbap: Bow-free covariance search and data de-correlation.PLoS computational biology · 2024Article
- Applications of Big Data and AI-Driven Technologies in CADD (Computer-Aided Drug Design).Methods in molecular biology (Clifton, N.J.) · 2024Article
- Metabolomic epidemiology offers insights into disease aetiology.Nature metabolism · 2023Review
- An epidemiological introduction to human metabolomic investigations.Trends in endocrinology and metabolism: TEM · 2023Review
- SpiderLearner: An ensemble approach to Gaussian graphical model estimation.Statistics in medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 1 country.
Funding
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 foundations of GGMs are introduced with the goal of enabling the researcher to draw practical conclusions by interpreting model results. Background literature is presented, emphasizing methods recently developed for high-dimensional applications such as genomics, proteomics, or metabolomics. The application of these methods is illustrated using a publicly available dataset of gene expression profiles from 578 participants with ovarian cancer in The Cancer Genome Atlas. Stand-alone code for the demonstration is available as an RMarkdown file at https://github.com/katehoffshutta/ggmTutorial.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.