ArticleInternational journal of molecular sciences2022
MSClustering: A Cytoscape Tool for Multi-Level Clustering of Biological Networks.
Article in International journal of molecular sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- System level identification and multidimensional analysis of hub genes reveal complex regulatory networks underlying human iPSC differentiation into motor neurons.Biochemistry and biophysics reports · 2026Article
- Mapping lncRNAs onto multilevel mRNA co‑expression modules in autism.Translational psychiatry · 2026Article
- Identification of the biomarkers associated with lipid metabolism and endoplasmic reticulum stress in pediatric sepsis through bioinformatics analysis and quantitative reverse transcriptase PCR (qRT-PCR) assay.European journal of medical research · 2026Article
- Phylogenetic network of infectious bronchitis virus: exploring the impact of migratory birds on viral clustering, evolution, and recombination.The veterinary quarterly · 2025Article
- Integrating multi-omic data to identify key genes and pathways involved in rice bacterial leaf streak disease.Scientific reports · 2025Article
- Accelerating crop improvement via integration of transcriptome-based network biology and genome editing.Planta · 2025Review
- An effective disease diagnostic model related to pyroptosis in ischemic cardiomyopathy.Journal of cellular and molecular medicine · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
MSClustering is an efficient software package for visualizing and analyzing complex networks in Cytoscape. Based on the distance matrix of a network that it takes as input, MSClustering automatically displays the minimum span clustering (MSC) of the network at various characteristic levels. To produce a view of the overall network structure, the app then organizes the multi-level results into an MSC tree. Here, we demonstrate the package's phylogenetic applications in studying the evolutionary relationships of complex systems, including 63 beta coronaviruses and 197 GPCRs. The validity of MSClustering for large systems has been verified by its clustering of 3481 enzymes. Through an experimental comparison, we show that MSClustering outperforms five different state-of-the-art methods in the efficiency and reliability of their clustering.
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Registered trials
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