ArticleBMC bioinformatics2014
Gene differential coexpression analysis based on biweight correlation and maximum clique.
Article in BMC bioinformatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
25 citing papers in PubMed, 64 citations in OpenAlex.
- Article
- LncRCD: A Comprehensive Database for Pan-Cancer Characterization of lncRNAs Related to 12 Regulated Cell Death Types.Computational and structural biotechnology journal · 2026Article
- Chromosome architecture affects virulence and competitiveness inScience advances · 2025Article
- Disruptions in gene interaction networks abolish host susceptibility to Trichostrongylus colubriformis infections in sheep.PLoS neglected tropical diseases · 2025Article
- Are the tools fit for purpose? Network inference algorithms evaluated on a simulated lipidomics network.Bioinformatics advances · 2025Article
- Online-adjusted evolutionary biclustering algorithm to identify significant modules in gene expression data.Briefings in bioinformatics · 2024Article
- Early life adversity is associated with differential gene expression in immune cells: A cluster-based analysis across an acute psychosocial stressor.Brain, behavior, and immunity · 2024Article
- Evolutionarily conserved gene expression patterns for affective disorders revealed using cross-species brain transcriptomic analyses in humans, rats and zebrafish.Scientific reports · 2022Article
- Pan-Cancer Bioinformatics Analysis of Gene UBE2C.Frontiers in genetics · 2022Article
- Dishevelled-Associated Activator of Morphogenesis 2 (DAAM2) Predicts the Immuno-Hot Phenotype in Pancreatic Adenocarcinoma.Frontiers in molecular biosciences · 2022Article
- [Identification of potential hub genes of Alzheimer's disease by weighted gene co-expression network analysis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2021Article
- A machine learning framework that integrates multi-omics data predicts cancer-related LncRNAs.BMC bioinformatics · 2021Article
- Disparate Metabolomic Responses to Fructose Consumption between Different Mouse Strains and the Role of Gut Microbiota.Metabolites · 2021Article
- A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources.Frontiers in genetics · 2021Article
- Predicting Protein Functions Based on Differential Co-expression and Neighborhood Analysis.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Host Genetic Background and Gut Microbiota Contribute to Differential Metabolic Responses to Fructose Consumption in Mice.The Journal of nutrition · 2020Article
- Erosion of Gene Co-expression Networks Reveal Deregulation of Immune System Processes in Late-Onset Alzheimer's Disease.Frontiers in neuroscience · 2020Article
- Construction, analysis and validation of co-expression network to understand stress adaptation in Deinococcus radiodurans R1.PloS one · 2020Article
- Systematic analysis of long non-coding RNA and mRNA expression changes in ApoE-deficient mice during atherosclerosis.Molecular and cellular biochemistry · 2019Article
- Comparative transcriptomics reveal developmental turning points during embryogenesis of a hemimetabolous insect, the damselfly Ischnura elegans.Scientific reports · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Differential coexpression analysis usually requires the definition of 'distance' or 'similarity' between measured datasets. Until now, the most common choice is Pearson correlation coefficient. However, Pearson correlation coefficient is sensitive to outliers. Biweight midcorrelation is considered to be a good alternative to Pearson correlation since it is more robust to outliers. In this paper, we introduce to use Biweight Midcorrelation to measure 'similarity' between gene expression profiles, and provide a new approach for gene differential coexpression analysis. Firstly, we calculate the biweight midcorrelation coefficients between all gene pairs. Then, we filter out non-informative correlation pairs using the 'half-thresholding' strategy and calculate the differential coexpression value of gene, The experimental results on simulated data show that the new approach performed better than three previously published differential coexpression analysis (DCEA) methods. Moreover, we use the maximum clique analysis to gene subset included genes identified by our approach and previously reported T2D-related genes, many additional discoveries can be found through our method.
Indexed as
Identifiers
What OpenQuestion holds
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.