ArticleNucleic acids research2023
DRAGON: Determining Regulatory Associations using Graphical models on multi-Omic Networks.
Article in Nucleic acids research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
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- Multimodal bioinformatic analyses of genome-scale expression beyond gene-centric differential expression.Briefings in bioinformatics · 2026Review
- Integration of high-throughput proteomic data and complementary omics layers with PriOmics.Genome research · 2026Article
- Integrative systematic review and transcriptomic -machine learning analysis of molecular signatures in metaplastic breast cancer.Ecancermedicalscience · 2026Article
- Comprehensive analysis of breast cancer oxidative stress related gene signature: a combination of bulk and single-cell RNA sequencing analysis.Mammalian genome : official journal of the International Mammalian Genome Society · 2025Article
- Reproducible processing of TCGA regulatory networks.GigaScience · 2025Article
- PhenoMultiOmics: an enzymatic reaction inferred multi-omics network visualization web server.Bioinformatics (Oxford, England) · 2024Article
- Bayesian inference of sample-specific coexpression networks.Genome research · 2024Article
- Spatial Cellular Networks from omics data with SpaCeNet.Genome research · 2024Article
- Higher-order correction of persistent batch effects in correlation networks.Bioinformatics (Oxford, England) · 2024Article
- Progress on network modeling and analysis of gut microecology: a review.Applied and environmental microbiology · 2024Review
- Methods for multi-omic data integration in cancer research.Frontiers in genetics · 2024Review
- The Network Zoo: a multilingual package for the inference and analysis of gene regulatory networks.Genome biology · 2023Article
- Gaussian graphical models with applications to omics analyses.Statistics in medicine · 2022Article
- GRAND: a database of gene regulatory network models across human conditions.Nucleic acids research · 2022Article
- Functional impact of multi-omic interactions in breast cancer subtypes.Frontiers in genetics · 2022Article
Corrections and comments
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
Abstract
The increasing quantity of multi-omic data, such as methylomic and transcriptomic profiles collected on the same specimen or even on the same cell, provides a unique opportunity to explore the complex interactions that define cell phenotype and govern cellular responses to perturbations. We propose a network approach based on Gaussian Graphical Models (GGMs) that facilitates the joint analysis of paired omics data. This method, called DRAGON (Determining Regulatory Associations using Graphical models on multi-Omic Networks), calibrates its parameters to achieve an optimal trade-off between the network's complexity and estimation accuracy, while explicitly accounting for the characteristics of each of the assessed omics 'layers.' In simulation studies, we show that DRAGON adapts to edge density and feature size differences between omics layers, improving model inference and edge recovery compared to state-of-the-art methods. We further demonstrate in an analysis of joint transcriptome - methylome data from TCGA breast cancer specimens that DRAGON can identify key molecular mechanisms such as gene regulation via promoter methylation. In particular, we identify Transcription Factor AP-2 Beta (TFAP2B) as a potential multi-omic biomarker for basal-type breast cancer. DRAGON is available as open-source code in Python through the Network Zoo package (netZooPy v0.8; netzoo.github.io).
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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 Socratic graph.