ArticleFrontiers in genetics2021
CoExp: A Web Tool for the Exploitation of Co-expression Networks.
Article in Frontiers in genetics, 2021. 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
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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.
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.
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Who cites it
25 citing papers in PubMed, 38 citations in OpenAlex.
- Transcriptomic Meta-Analysis as a Framework for Robust Cross-Study Biological Inference.International journal of molecular sciences · 2026Review
- Genetic drivers of progression in Alzheimer's disease are distinct from disease risk.Alzheimer's research & therapy · 2026Article
- Network-based exploration of 4-(phenylsulfonyl)morpholine molecules for metastatic triple-negative breast cancer suppression.PLoS computational biology · 2026Article
- Informed spatially aware patterns for multiplexed immunofluorescence data.Scientific reports · 2026Article
- SGCRNA: spectral clustering-guided co-expression network analysis without scale-free constraints for multi-omic data.Briefings in bioinformatics · 2026Article
- Genetic drivers of progression in Alzheimer's disease are distinct from disease risk.medRxiv : the preprint server for health sciences · 2025Article
- Utilizing rat kidney gene co-expression networks to enhance safety assessment biomarker identification and human translation.iScience · 2025Article
- Integrating brain proteomes and genetics to identify novel risk genes in chronic widespread musculoskeletal pain.Scientific reports · 2025Article
- Genome-wide epistasis analysis reveals significant epistatic signals associated with Parkinson's disease risk.Brain : a journal of neurology · 2025Article
- Increased burden of rare risk variants across gene expression networks predisposes to sporadic Parkinson's disease.Cell reports · 2025Article
- From Data to Cure: A Comprehensive Exploration of Multi-omics Data Analysis for Targeted Therapies.Molecular biotechnology · 2025Review
- Article
- Role of Epigenetic Factors in Determining the Biological Behavior and Prognosis of Hepatocellular Carcinoma.Diagnostics (Basel, Switzerland) · 2024Article
- Targeted co-expression networks for the study of traits.Scientific reports · 2024Article
- CoGTEx: Unscaled system-level coexpression estimation from GTEx data forecast novel functional gene partners.PloS one · 2024Article
- Cell-free RNA signatures predict Alzheimer's disease.iScience · 2023Article
- The non-specific lethal complex regulates genes and pathways genetically linked to Parkinson's disease.Brain : a journal of neurology · 2023Article
- Genetic topography and cortical cell loss in Huntington's disease link development and neurodegeneration.Brain : a journal of neurology · 2023Article
- Identifying causal genes for migraine by integrating the proteome and transcriptome.The journal of headache and pain · 2023Article
- Predicting brain-regional gene regulatory networks from multi-omics for Alzheimer's disease phenotypes and Covid-19 severity.Human molecular genetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 5 institutions in 5 countries.
Funding
Abstract
Gene co-expression networks are a powerful type of analysis to construct gene groupings based on transcriptomic profiling. Co-expression networks make it possible to discover modules of genes whose mRNA levels are highly correlated across samples. Subsequent annotation of modules often reveals biological functions and/or evidence of cellular specificity for cell types implicated in the tissue being studied. There are multiple ways to perform such analyses with weighted gene co-expression network analysis (WGCNA) amongst one of the most widely used R packages. While managing a few network models can be done manually, it is often more advantageous to study a wider set of models derived from multiple independently generated transcriptomic data sets (e.g., multiple networks built from many transcriptomic sources). However, there is no software tool available that allows this to be easily achieved. Furthermore, the visual nature of co-expression networks in combination with the coding skills required to explore networks, makes the construction of a web-based platform for their management highly desirable. Here, we present the CoExp Web application, a user-friendly online tool that allows the exploitation of the full collection of 109 co-expression networks provided by the CoExpNets suite of R packages. We describe the usage of CoExp, including its contents and the functionality available through the family of CoExpNets packages. All the tools presented, including the web front- and back-ends are available for the research community so any research group can build its own suite of networks and make them accessible through their own CoExp Web application. Therefore, this paper is of interest to both researchers wishing to annotate their genes of interest across different brain network models and specialists interested in the creation of GCNs looking for a tool to appropriately manage, use, publish, and share their networks in a consistent and productive manner.
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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.