ArticleBMC bioinformatics2016
NEAT: an efficient network enrichment analysis test.
Article in BMC bioinformatics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 33 papers, 1 of them a synthesis that pooled it.
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Who cites it
33 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
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- A practical guide to functional enrichment analysis.Frontiers in bioinformatics · 2026Article
- A novel multivariate framework for functional gene networks enrichment analysis.Frontiers in genetics · 2026Article
- Powerful gene network enrichment analysis and its application to severe COVID-19 gene network.Briefings in bioinformatics · 2025Article
- A Machine-Learning Approach Identifies Rejuvenating Interventions in the Human Brain.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Gene behaviors-based network enrichment analysis and its application to reveal immune disease pathways enriched with COVID-19 severity-specific gene networks.Bioinformatics (Oxford, England) · 2025Article
- Computational Drug Repositioning in Cardiorenal Disease: Opportunities, Challenges, and Approaches.Proteomics · 2025Article
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- Transcriptional reprogramming during human osteoclast differentiation identifies regulators of osteoclast activity.Bone research · 2024Article
- Benchmarking enrichment analysis methods with the disease pathway network.Briefings in bioinformatics · 2024Article
- ActivePPI: quantifying protein-protein interaction network activity with Markov random fields.Bioinformatics (Oxford, England) · 2023Article
- Hearing of malaria mosquitoes is modulated by a beta-adrenergic-like octopamine receptor which serves as insecticide target.Nature communications · 2023Article
- Increased body mass index is linked to systemic inflammation through altered chromatin co-accessibility in human preadipocytes.Nature communications · 2023Article
- Network Crosstalk as a Basis for Drug Repurposing.Frontiers in genetics · 2022Article
- Identification of CXCL10 as a Prognostic Biomarker for Clear Cell Renal Cell Carcinoma.Frontiers in oncology · 2022Article
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3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundNetwork enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions.
resultsWe propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves.
conclusionsNEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).
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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.