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Cited by 633Bioinformatics and Genomic NetworksComplex Network Analysis TechniquesGene Regulatory Network Analysis

Gene network interconnectedness and the generalized topological overlap measure

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Yip, Andy M, Horvath, Steve·2007·BMC Bioinformatics
DOI 10.1186/1471-2105-8-22PMC PMC1797055
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Abstract

BACKGROUND: Network methods are increasingly used to represent the interactions of genes and/or proteins. Genes or proteins that are directly linked may have a similar biological function or may be part of the same biological pathway. Since the information on the connection (adjacency) between 2 nodes may be noisy or incomplete, it can be desirable to consider alternative measures of pairwise interconnectedness. Here we study a class of measures that are proportional to the number of neighbors that a pair of nodes share in common. For example, the topological overlap measure by Ravasz et al. 1 can be interpreted as a measure of agreement between the m = 1 step neighborhoods of 2 nodes. Several studies have shown that two proteins having a higher topological overlap are more likely to belong to the same functional class than proteins having a lower topological overlap. Here we address the question whether a measure of topological overlap based on higher-order neighborhoods could give rise to a more robust and sensitive measure of interconnectedness. RESULTS: We generalize the topological overlap measure from m = 1 step neighborhoods to m > or = 2 step neighborhoods. This allows us to define the m-th order generalized topological overlap measure (GTOM) by (i) counting the number of m-step neighbors that a pair of nodes share and (ii) normalizing it to take a value between 0 and 1. Using theoretical arguments, a yeast co-expression network application, and a fly protein network application, we illustrate the usefulness of the proposed measure for module detection and gene neighborhood analysis. CONCLUSION: Topological overlap can serve as an important filter to counter the effects of spurious or missing connections between network nodes. The m-th order topological overlap measure allows one to trade-off sensitivity versus specificity when it comes to defining pairwise interconnectedness and network modules.

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  • Co-expression networks are based on transformed pairwise correlations of input features, resulting in a quantitative measure of relatedness between genes.

    “Co-expression networks are based on transformed pairwise correlations of input features, resulting in a quantitative measure of relatedness between genes.1,2”

    From hdWGCNA identifies co-expression networks in high-dimensional transcriptomics data (2023) · cites this paper as [2]

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Research works printed but not linked: 25

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Cited by

Sorted by citations

Authors
Yip, A. M., & Horvath, S.
Affiliations
  • National University of Singapore (SG)
  • University of California, Los Angeles (US)
Corresponding author
Horvath, Steve
ORCID
Venue
BMC Bioinformatics
Publisher
Springer Science and Business Media LLC
Published
2007-01-24
Volume
8
Issue
1
Pages
22-22
ISSN
1471-2105 (ISSN-L)
DOI
10.1186/1471-2105-8-22
PMC
PMC1797055
OpenAlex
W1502421007
Cited by
633
References
7
Citations by year
2026: 13 · 2025: 26 · 2024: 38 · 2023: 47 · 2022: 66 · 2021: 65 · 2020: 60 · 2019: 55 · 2018: 44 · 2017: 35 · 2016: 28 · 2015: 23 · 2014: 21 · 2013: 26 · 2012: 20
FWCI
5.97
Type
journal article
Language
en
Open access
Licence
Locations
  • BMC Bioinformatics (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • National University of Singapore (submittedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
National Institute of Allergy and Infectious Diseases (U19AI063603)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Physical Sciences › Physics and Astronomy › Statistical and Nonlinear Physics › Complex Network Analysis Techniques
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
Keywords
gene co-expression network, protein-protein interaction network, network module detection, sensitivity and specificity trade-off
MeSH terms
Algorithms, Animals, Drosophila melanogaster, Fungal Proteins, Models, Genetic, Models, Theoretical, Models, Statistical, Cluster Analysis, Computational Biology, Protein Interaction Mapping, Gene Regulatory Networks

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