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

Biology direct

PubMedID: 24947308

Azuaje FJ. Selecting biologically informative genes in co-expression networks with a centrality score. Biol Direct. 2014;9(1):12.
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 Kandu[latin small letter l with stroke]a (nominated by David P Kreil) and Christine Wells.