ArticleBioinformatics (Oxford, England)2017
EGAD: ultra-fast functional analysis of gene networks.
Article in Bioinformatics (Oxford, England), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 4 of them syntheses that pooled it.
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Who cites it
50 citing papers in PubMed, 4 syntheses or guidelines pooled it.
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- Mapping protein states and interactions across the tree of life with co-fractionation mass spectrometry.Nature communications · 2023Pooled it
- Meta-analysis defines principles for the design and analysis of co-fractionation mass spectrometry experiments.Nature methods · 2021Pooled it
- Network aggregation improves gene function prediction of grapevine gene co-expression networks.Plant molecular biology · 2020Pooled it
- A consensus atlas of human brain development defines cell type-specific maturation trajectories across the lifespan.bioRxiv : the preprint server for biology · 2026Article
- Genes associated with translation and oxidative phosphorylation as components of the translational response in nodulated and water-restricted soybean.BMC plant biology · 2026Article
- The impact of sex on the immune system explored at the single-cell level.American journal of human genetics · 2026Article
- Translation efficiency covariation identifies conserved coordination patterns across cell types.Nature biotechnology · 2026Article
- Monocyte Lineage Expansion Drives Transcriptomic Individuality in Genetically Identical Armadillo Quadruplets.bioRxiv : the preprint server for biology · 2026Article
- Cell type-agnostic transcriptomic signatures enable uniform comparisons of neural maturation.PLoS biology · 2026Article
- Inferring Gene Networks from High-Throughput Transcriptomes.Methods in molecular biology (Clifton, N.J.) · 2026Article
- Modular inflammation network discovery from large-scale phenotypic screening in genetically heterogeneous mouse brains.Journal of neuroinflammation · 2025Article
- LEGEND: Identifying Co-expressed Genes in Multimodal Transcriptomic Sequencing Data.Genomics, proteomics & bioinformatics · 2025Article
- The Hydronephrosis Severity Index guides paediatric antenatal hydronephrosis management based on artificial intelligence applied to ultrasound images alone.Scientific reports · 2024Article
- Higher-order correction of persistent batch effects in correlation networks.Bioinformatics (Oxford, England) · 2024Article
- Massively integrated coexpression analysis reveals transcriptional regulation, evolution and cellular implications of the yeast noncanonical translatome.Genome biology · 2024Article
- Defining Transcriptomic Heterogeneity between Left and Right Ventricle-Derived Cardiac Fibroblasts.Cells · 2024Article
- Exploring large-scale gene coexpression networks in peach (Horticulture research · 2024Article
- CryptoCEN: A Co-Expression Network for Cryptococcus neoformans reveals novel proteins involved in DNA damage repair.PLoS genetics · 2024Article
- Preservation of co-expression defines the primary tissue fidelity of human neural organoids.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Summary: Evaluating gene networks with respect to known biology is a common task but often a computationally costly one. Many computational experiments are difficult to apply exhaustively in network analysis due to run-times. To permit high-throughput analysis of gene networks, we have implemented a set of very efficient tools to calculate functional properties in networks based on guilt-by-association methods. ( xtending ' uilt-by- ssociation' by egree) allows gene networks to be evaluated with respect to hundreds or thousands of gene sets. The methods predict novel members of gene groups, assess how well a gene network groups known sets of genes, and determines the degree to which generic predictions drive performance. By allowing fast evaluations, whether of random sets or real functional ones, provides the user with an assessment of performance which can easily be used in controlled evaluations across many parameters. Availability and Implementation: The software package is freely available at https://github.com/sarbal/EGAD and implemented for use in R and Matlab. The package is also freely available under the LGPL license from the Bioconductor web site ( http://bioconductor.org ). Contact: JGillis@cshl.edu. 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.