ArticleBMC bioinformatics2004
Quantifying the relationship between co-expression, co-regulation and gene function.
Article in BMC bioinformatics, 2004. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 167 papers, 4 of them syntheses that pooled it.
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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.
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Who cites it
167 citing papers in PubMed, 4 syntheses or guidelines pooled it, 364 citations in OpenAlex.
- Network-Based Integration of GWAS and Gene Expression Identifies a HOX-Centric Network Associated with Serous Ovarian Cancer Risk.Cancer epidemiology, biomarkers & prevention : a publication of the American Association for Cancer Research, cosponsored by the American Society of Preventive Oncology · 2015Pooled it
- Ranking candidate genes of esophageal squamous cell carcinomas based on differentially expressed genes and the topological properties of the co-expression network.European journal of medical research · 2014Pooled it
- Integrative transcriptome analysis reveals dysregulation of canonical cancer molecular pathways in placenta leading to preeclampsia.Scientific reports · 2013Pooled it
- Boolean implication networks derived from large scale, whole genome microarray datasets.Genome biology · 2008Pooled it
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- Topological and functional characterization of human translation efficiency covariation network.Bioinformatics (Oxford, England) · 2025Article
- Evolutionary divergence between homologous X-Y chromosome genes shapes sex-biased biology.Nature ecology & evolution · 2025Article
- Single-cell transcriptomes reveal cell-type-specific and sample-specific gene function in human cancer.Heliyon · 2025Article
- A Comparative Study of Gene Co-Expression Thresholding Algorithms.Journal of computational biology : a journal of computational molecular cell biology · 2024Article
- Unveiling Shared Immune Responses in Porcine Alveolar Macrophages during ASFV and PRRSV Infection Using Single-Cell RNA-seq.Microorganisms · 2024Article
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- Gene communities in co-expression networks across different tissues.PLoS computational biology · 2023Article
- Identification of a novel gene signature for neuroblastoma differentiation using a Boolean implication network.Genes, chromosomes & cancer · 2023Article
- Gene Functional Networks from Time Expression Profiles: A Constructive Approach Demonstrated in Chili Pepper (Plants (Basel, Switzerland) · 2023Article
107 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
backgroundIt is thought that genes with similar patterns of mRNA expression and genes with similar functions are likely to be regulated via the same mechanisms. It has been difficult to quantitatively test these hypotheses on a large scale because there has been no general way of determining whether genes share a common regulatory mechanism. Here we use data from a recent genome wide binding analysis in combination with mRNA expression data and existing functional annotations to quantify the likelihood that genes with varying degrees of similarity in mRNA expression profile or function will be bound by a common transcription factor.
resultsGenes with strongly correlated mRNA expression profiles are more likely to have their promoter regions bound by a common transcription factor. This effect is present only at relatively high levels of expression similarity. In order for two genes to have a greater than 50% chance of sharing a common transcription factor binder, the correlation between their expression profiles (across the 611 microarrays used in our study) must be greater than 0.84. Genes with similar functional annotations are also more likely to be bound by a common transcription factor. Combining mRNA expression data with functional annotation results in a better predictive model than using either data source alone.
conclusionsWe demonstrate how mRNA expression data and functional annotations can be used together to estimate the probability that genes share a common regulatory mechanism. Existing microarray data and known functional annotations are sufficient to identify only a relatively small percentage of co-regulated genes.
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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.