Selecting biologically informative genes in co-expression networks with a centrality score

Francisco J. Azuaje*

*Corresponding author for this work

    Research output: Contribution to journalArticleResearchpeer-review

    45 Citations (Scopus)


    Background: Measures of node centrality in biological networks are useful to detect genes with critical functional roles. In gene co-expression networks, highly connected genes (i.e., candidate hubs) have been associated with key disease-related pathways. Although different approaches to estimating gene centrality are available, their potential biological relevance in gene co-expression networks deserves further investigation. Moreover, standard measures of gene centrality focus on binary interaction networks, which may not always be suitable in the context of co-expression networks. Here, I also investigate a method that identifies potential biologically meaningful genes based on a weighted connectivity score and indicators of statistical relevance.Results: The method enables a characterization of the strength and diversity of co-expression associations in the network. It outperformed standard centrality measures by highlighting more biologically informative genes in different gene co-expression networks and biological research domains. As part of the illustration of the gene selection potential of this approach, I present an application case in zebrafish heart regeneration. The proposed technique predicted genes that are significantly implicated in cellular processes required for tissue regeneration after injury.Conclusions: A method for selecting biologically informative genes from gene co-expression networks is provided, together with free open software.Reviewers: This article was reviewed by Anthony Almudevar, Maciej M Kańduła (nominated by David P Kreil) and Christine Wells.

    Original languageEnglish
    Article number12
    JournalBiology Direct
    Issue number1
    Publication statusPublished - 19 Jun 2014


    • Cancer
    • Centrality scores
    • Gene co-expression networks
    • Heart regeneration
    • Microarrays
    • Network hubs
    • RNA-Seq
    • Weighted networks
    • Zebrafish


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