Artikel ; Online: Selecting biologically informative genes in co-expression networks with a centrality score.
Biology direct
2014 Band 9, Seite(n) 12
Abstract: 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 ... ...
Abstract | 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. |
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Mesh-Begriff(e) | Animals ; Gene Expression Profiling/methods ; Gene Regulatory Networks ; Heart/physiology ; Myocardium/metabolism ; Regeneration ; Zebrafish/genetics ; Zebrafish/metabolism ; Zebrafish/physiology |
Sprache | Englisch |
Erscheinungsdatum | 2014-06-19 |
Erscheinungsland | England |
Dokumenttyp | Evaluation Study ; Journal Article ; Research Support, Non-U.S. Gov't |
ISSN | 1745-6150 |
ISSN (online) | 1745-6150 |
DOI | 10.1186/1745-6150-9-12 |
Datenquelle | MEDical Literature Analysis and Retrieval System OnLINE |
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