ArticlePLoS computational biology2015
Multiscale Embedded Gene Co-expression Network Analysis.
Article in PLoS computational biology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 235 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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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.
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Who cites it
235 citing papers in PubMed, 4 syntheses or guidelines pooled it, 409 citations in OpenAlex.
- Comprehensive meta-analysis reveals distinct gene expression signatures of MASLD progression.Life science alliance · 2024Pooled it
- Guidelines for bioinformatics of single-cell sequencing data analysis in Alzheimer's disease: review, recommendation, implementation and application.Molecular neurodegeneration · 2022Pooled it
- Identifying correlations driven by influential observations in large datasets.Briefings in bioinformatics · 2022Pooled it
- Meta-Analysis of the Alzheimer's Disease Human Brain Transcriptome and Functional Dissection in Mouse Models.Cell reports · 2020Pooled it
- Molecular Transducers of Human Skeletal Muscle Remodeling under Different Loading States.Cell reports · 2020Trial
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- Integrative plasma proteomic network analysis identifies physical activity-associated protein modules potentially mediating type 2 diabetes risk.Cardiovascular diabetology · 2026Article
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- Regulatory divergence of homoeologs underlies network optimization for fiber improvement in domesticated cotton.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Gene network analysis predicts the primary regulators of ABA-dependent transcriptional activation and repression in Populus roots.PLoS genetics · 2026Article
- MFAP5iScience · 2026Article
- Mapping lncRNAs onto multilevel mRNA co‑expression modules in autism.Translational psychiatry · 2026Article
- Molecular Networks and Key Regulators Underlying Resilience of the Human Brain to Aging and Dementia.Biomolecules · 2026Article
- Select microbial metabolites promote tau aggregation in a murine tauopathy model.Nature communications · 2026Article
- Transcriptional profiles of antidepressant resistance across the corticolimbic pathway of chronically stressed mice.Neuropsychopharmacology : official publication of the American College of Neuropsychopharmacology · 2026Article
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- Deciphering the genetic basis of wheat germination under ZnO nano priming and drought stress through integrated QTL mapping and network analyses.BMC plant biology · 2026Article
175 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.
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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.