ReviewStatistical methods & applications2022
Bayesian graphical models for modern biological applications.
Review in Statistical methods & applications, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Spatially Varying Graphical Models for Cell-Cell Interaction Networks in Multiplexed Tissue Imaging.bioRxiv : the preprint server for biology · 2026Article
- Exploring cooperation among social pathogens: a computational perspective.FEMS microbiology reviews · 2026Review
- Latent space-based network analysis for brain-behavior linking in neuroimaging.Nature methods · 2026Article
- A modeling framework for detecting and leveraging node-level information in Bayesian network inference.Biostatistics (Oxford, England) · 2025Article
- Connectivity Regression.Biostatistics (Oxford, England) · 2025Article
- A probabilistic modeling framework for genomic networks incorporating sample heterogeneity.Cell reports methods · 2025Article
- Network Analysis of Behavioral Activation/Inhibition Systems and Brain Volume in Individuals With and Without Major Depressive Disorder or Social Anxiety Disorder.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2024Article
- Causal association study of the dynamic development of the metabolic syndrome based on longitudinal data.Scientific reports · 2024Article
- Probabilistic edge inference of gene networks with markov random field-based bayesian learning.Frontiers in genetics · 2022Article
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.
Funding
Abstract
Graphical models are powerful tools that are regularly used to investigate complex dependence structures in high-throughput biomedical datasets. They allow for holistic, systems-level view of the various biological processes, for intuitive and rigorous understanding and interpretations. In the context of large networks, Bayesian approaches are particularly suitable because it encourages sparsity of the graphs, incorporate prior information, and most importantly account for uncertainty in the graph structure. These features are particularly important in applications with limited sample size, including genomics and imaging studies. In this paper, we review several recently developed techniques for the analysis of large networks under non-standard settings, including but not limited to, multiple graphs for data observed from multiple related subgroups, graphical regression approaches used for the analysis of networks that change with covariates, and other complex sampling and structural settings. We also illustrate the practical utility of some of these methods using examples in cancer genomics and neuroimaging.
Indexed as
Identifiers
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.