cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. These methods have been successfully applied in various biological contexts, e.g. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures.
For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples.
Correlation networks are increasingly being used in bioinformatics applications.
