ArticleBMC genomics2019
GScluster: network-weighted gene-set clustering analysis.
Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 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
13 citing papers in PubMed.
- Lymph node extracellular matrix skews the immune function of human fibroblastic reticular cells.iScience · 2026Article
- GeDi: simplifying gene set distances for enhanced omics interpretation in R/Bioconductor.BMC bioinformatics · 2025Article
- Cluefish: mining the dark matter of transcriptional data series with over-representation analysis enhanced by aggregated biological prior knowledge.NAR genomics and bioinformatics · 2025Article
- Pathway Analysis Interpretation in the Multi-Omic Era.Biotech (Basel (Switzerland)) · 2025Review
- GSK3β serves as a novel therapeutic target in patients with type 2 diabetes mellitus complicated by colorectal cancer.American journal of translational research · 2025Article
- vissE: a versatile tool to identify and visualise higher-order molecular phenotypes from functional enrichment analysis.BMC bioinformatics · 2024Article
- TAK-981, a SUMOylation inhibitor, suppresses AML growth immune-independently.Blood advances · 2023Article
- Article
- Benefits and Challenges of Pre-clustered Network-Based Pathway Analysis.Frontiers in genetics · 2022Article
- GeneTonic: an R/Bioconductor package for streamlining the interpretation of RNA-seq data.BMC bioinformatics · 2021Article
- Article
- GeneSetCluster: a tool for summarizing and integrating gene-set analysis results.BMC bioinformatics · 2020Article
- Bioinformatics services for analyzing massive genomic datasets.Genomics & informatics · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
backgroundGene-set analysis (GSA) has been commonly used to identify significantly altered pathways or functions from omics data. However, GSA often yields a long list of gene-sets, necessitating efficient post-processing for improved interpretation. Existing methods cluster the gene-sets based on the extent of their overlap to summarize the GSA results without considering interactions between gene-sets.
resultsHere, we presented a novel network-weighted gene-set clustering that incorporates both the gene-set overlap and protein-protein interaction (PPI) networks. Three examples were demonstrated for microarray gene expression, GWAS summary, and RNA-sequencing data to which different GSA methods were applied. These examples as well as a global analysis show that the proposed method increases PPI densities and functional relevance of the resulting clusters. Additionally, distinct properties of gene-set distance measures were compared. The methods are implemented as an R/Shiny package GScluster that provides gene-set clustering and diverse functions for visualization of gene-sets and PPI networks.
conclusionsNetwork-weighted gene-set clustering provides functionally more relevant gene-set clusters and related network analysis.
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.