In the gene co-expression network, each gene corresponds to a node, and if the pairwise expression similarity score of a pair of nodes is higher than a certain threshold, a pair of nodes will be connected by an undirected edge. Constructing a co-expression network from gene expression data sets has become a widely used alternative to conventional analysis methods. Large-scale gene co-expression networks have been applied, for example, to prove that functionally related genes are usually co-expressed in various data sets and different organisms. By constructing co-expression networks for different conditions (such as normal and cancerous states), it is possible to study changes in disease-mediated network connections.