Evidence map›Paper›PMID 27466623›Full record

ArticleBioinformatics (Oxford, England)2016

NCMine: Core-peripheral based functional module detection using near-clique mining.

Shu Tadaka, Kengo Kinoshita

Abstract read
In one paragraph

Article in Bioinformatics (Oxford, England), 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.

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0cells of the map it votes in
16citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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3 · Its place in the literature

Who cites it

16 citing papers in PubMed.

  1. Review
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  3. Article
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  6. Review
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  10. Modularity in Biological Networks.Frontiers in genetics · 2021
    Review
  11. Article
  12. Article
  13. Transcriptome and Network Analyses of Heterostyly inPlants (Basel, Switzerland) · 2020
    Article
  14. Article
  15. Article
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4 · The record

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5 · Who and what money

Authors and funding

2 authors.

Shu TadakaGraduate School of Information Sciences, Tohoku University, Sendai, Japan.
Kengo KinoshitaGraduate School of Information Sciences, Tohoku University, Sendai, Japan.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

motivationThe identification of functional modules from protein-protein interaction (PPI) networks is an important step toward understanding the biological features of PPI networks. The detection of functional modules in PPI networks is often performed by identifying internally densely connected subnetworks, and often produces modules with "core" and "peripheral" proteins. The core proteins are the ones having dense connections to each other in a module. The difference between core and peripheral proteins is important to understand the functional roles of proteins in modules, but there are few methods to explicitly elucidate the internal structure of functional modules at gene level.

resultsWe propose NCMine, which is a novel network clustering method and visualization tool for the core-peripheral structure of functional modules. It extracts near-complete subgraphs from networks based on a node-weighting scheme using degree centrality, and reports subgroups as functional modules. We implemented this method as a plugin of Cytoscape, which is widely used to visualize and analyze biological networks. The plugin allows users to extract functional modules from PPI networks and interactively filter modules of interest. We applied the method to human PPI networks, and found several examples with the core-peripheral structure of modules that may be related to cancer development. AVAILABILITY AND IMPLEMENTATION: The Cytoscape plugin and tutorial are available at Cytoscape AppStore. (http://apps.cytoscape.org/apps/ncmine). CONTACT: kengo@ecei.tohoku.ac.jpSupplementary information: Supplementary data are available at Bioinformatics online.

Indexed as

AlgorithmsProtein Interaction MappingAnimalsCluster AnalysisHumansProtein Interaction Maps

Identifiers

PMID27466623
PMCPMC5181566

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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.