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

Area of Science:

• Systems biology
• Bioinformatics
• Network analysis

Background:

• Node centrality measures in biological networks identify genes with critical roles.
• Hub genes in gene co-expression networks are linked to disease pathways.
• Existing centrality measures often focus on binary networks, limiting their application to co-expression data.

Purpose of the Study:

• To investigate a novel method for identifying biologically meaningful genes in gene co-expression networks.
• To assess the biological relevance of gene centrality measures in co-expression networks.
• To develop a method utilizing weighted connectivity and statistical relevance for gene selection.

Main Methods:

• Development of a gene selection method based on weighted connectivity scores.
• Evaluation of the method's performance against standard centrality measures.
• Application of the method to gene co-expression networks across different biological domains.

Main Results:

• The proposed method characterizes the strength and diversity of co-expression associations.
• It successfully identified more biologically informative genes compared to standard centrality measures.
• An application in zebrafish heart regeneration demonstrated the method's ability to predict genes crucial for tissue repair.

Conclusions:

• A new method for selecting biologically informative genes from gene co-expression networks has been developed.
• The method offers improved biological relevance compared to traditional approaches.
• Free open-source software for this method is available.