ArticleBioinformatics (Oxford, England)2015
DINGO: differential network analysis in genomics.
Article in Bioinformatics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 61 papers.
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Who cites it
61 citing papers in PubMed, 125 citations in OpenAlex.
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- Integrative gene and isoform co-expression networks reveal regulatory rewiring in stress-related psychiatric disorders.iScience · 2025Article
- Aggregation of recount3 RNA-seq data improves inference of consensus and tissue-specific gene coexpression networks.Genome research · 2025Article
- Plasma and ovarian metabolomic responses to chronic stress in female mice.Metabolomics : Official journal of the Metabolomic Society · 2025Article
- MORE interpretable multi-omic regulatory networks to characterise phenotypes.Briefings in bioinformatics · 2025Article
- Persistent PTSD symptoms are associated with plasma metabolic alterations relevant to long-term health: A metabolome-wide investigation in women.Psychological medicine · 2025Article
- Exploring glioma heterogeneity through omics networks: from gene network discovery to causal insights and patient stratification.BioData mining · 2024Article
- ONDSA: a testing framework based on Gaussian graphical models for differential and similarity analysis of multiple omics networks.Briefings in bioinformatics · 2024Article
- Non parametric differential network analysis: a tool for unveiling specific molecular signatures.BMC bioinformatics · 2024Article
- Persistent PTSD symptoms are associated with plasma metabolic alterations relevant to long-term health: A metabolome-wide investigation in women.medRxiv : the preprint server for health sciences · 2024Article
- DDN3.0: determining significant rewiring of biological network structure with differential dependency networks.Bioinformatics (Oxford, England) · 2024Article
- Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.Journal of the American Statistical Association · 2024Article
- PRANA: an R package for differential co-expression network analysis with the presence of additional covariates.BMC genomics · 2023Article
- Kernelized multiview signed graph learning for single-cell RNA sequencing data.BMC bioinformatics · 2023Article
- Large-scale correlation network construction for unraveling the coordination of complex biological systems.Nature computational science · 2023Article
- A pseudo-value regression approach for differential network analysis of co-expression data.BMC bioinformatics · 2023Article
- A Bayesian approach to differential edges with probabilistic interactions: applications in association and classification.Bioinformatics advances · 2023Article
- RCFGL: Rapid Condition adaptive Fused Graphical Lasso and application to modeling brain region co-expression networks.PLoS computational biology · 2023Article
1 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
motivationCancer progression and development are initiated by aberrations in various molecular networks through coordinated changes across multiple genes and pathways. It is important to understand how these networks change under different stress conditions and/or patient-specific groups to infer differential patterns of activation and inhibition. Existing methods are limited to correlation networks that are independently estimated from separate group-specific data and without due consideration of relationships that are conserved across multiple groups.
methodWe propose a pathway-based differential network analysis in genomics (DINGO) model for estimating group-specific networks and making inference on the differential networks. DINGO jointly estimates the group-specific conditional dependencies by decomposing them into global and group-specific components. The delineation of these components allows for a more refined picture of the major driver and passenger events in the elucidation of cancer progression and development.
resultsSimulation studies demonstrate that DINGO provides more accurate group-specific conditional dependencies than achieved by using separate estimation approaches. We apply DINGO to key signaling pathways in glioblastoma to build differential networks for long-term survivors and short-term survivors in The Cancer Genome Atlas. The hub genes found by mRNA expression, DNA copy number, methylation and microRNA expression reveal several important roles in glioblastoma progression. AVAILABILITY AND IMPLEMENTATION: R Package at: odin.mdacc.tmc.edu/∼vbaladan. CONTACT: veera@mdanderson.org SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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 OpenQuestion graph.