ArticleGenes2021
K-Module Algorithm: An Additional Step to Improve the Clustering Results of WGCNA Co-Expression Networks.
Article in Genes, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 1 of them a synthesis that pooled it.
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Who cites it
18 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Network medicine approaches for identification of novel prognostic systems biomarkers and drug candidates for papillary thyroid carcinoma.Journal of cellular and molecular medicine · 2023Pooled it
- Anoctamin 5 as a protective factor in prostate cancer: Insights from WGCNA, machine learning, and experimental analysis, with a focus on the anoctamin family.Translational oncology · 2026Article
- NOVAGene: a nonlinear approach to improve biological resolution in weighted gene co-expression networks for low-variability datasets.Frontiers in bioinformatics · 2026Article
- Reconciling multiple connectivity-based systems biology methods for drug repurposing.Briefings in bioinformatics · 2025Review
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- Isolation and identification of core-genes in barley roots under low phosphorus stress.Molecular biology reports · 2025Article
- Hypergraph-based analysis of weighted gene co-expression hypernetwork.Frontiers in genetics · 2025Article
- SGCP: a spectral self-learning method for clustering genes in co-expression networks.BMC bioinformatics · 2024Article
- Construction of a prognostic prediction model for renal clear cell carcinoma combining clinical traits.Scientific reports · 2023Article
- A functional gene module identification algorithm in gene expression data based on genetic algorithm and gene ontology.BMC genomics · 2023Article
- Integrated bioinformatic analyses investigate macrophage-M1-related biomarkers and tuberculosis therapeutic drugs.Frontiers in genetics · 2023Article
- KISL: knowledge-injected semi-supervised learning for biological co-expression network modules.Frontiers in genetics · 2023Article
- A hypoxia-related prognostic model predicts overall survival and treatment response in hepatocellular carcinoma.Bioscience reports · 2022Article
- Identification of biomarkers related to neutrophils and two molecular subtypes of systemic lupus erythematosus.BMC medical genomics · 2022Article
- Molecular Regulatory Mechanisms Drive Emergent Pathogenetic Properties ofMicroorganisms · 2022Review
- Distance correlation application to gene co-expression network analysis.BMC bioinformatics · 2022Article
- Spatial Transcriptomic Analysis Using R-Based Computational Machine Learning Reveals the Genetic Profile of Yang or Yin Deficiency Syndrome in Chinese Medicine Theory.Evidence-based complementary and alternative medicine : eCAM · 2022Article
- A Novel Calibration Step in Gene Co-Expression Network Construction.Frontiers in bioinformatics · 2021Article
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4 authors.
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Abstract
Among biological networks, co-expression networks have been widely studied. One of the most commonly used pipelines for the construction of co-expression networks is weighted gene co-expression network analysis (WGCNA), which can identify highly co-expressed clusters of genes (modules). WGCNA identifies gene modules using hierarchical clustering. The major drawback of hierarchical clustering is that once two objects are clustered together, it cannot be reversed; thus, re-adjustment of the unbefitting decision is impossible. In this paper, we calculate the similarity matrix with the distance correlation for WGCNA to construct a gene co-expression network, and present a new approach called the k-module algorithm to improve the WGCNA clustering results. This method can assign all genes to the module with the highest mean connectivity with these genes. This algorithm re-adjusts the results of hierarchical clustering while retaining the advantages of the dynamic tree cut method. The validity of the algorithm is verified using six datasets from microarray and RNA-seq data. The k-module algorithm has fewer iterations, which leads to lower complexity. We verify that the gene modules obtained by the k-module algorithm have high enrichment scores and strong stability. Our method improves upon hierarchical clustering, and can be applied to general clustering algorithms based on the similarity matrix, not limited to gene co-expression network analysis.
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