In most the biological contexts, examining gene expressions at the genomic level gives
more accurate results than examining genes individually. It can improve understanding of …

The application of complex network modeling to analyze large co-expression data sets has
gained traction during the last decade. In particular, the use of the weighted gene co …

Biomolecular networks are often assumed to be scale-free hierarchical networks. The
weighted gene co-expression network analysis (WGCNA) treats gene co-expression …

Gene co-expression networks are increasingly used to explore the system-level functionality
of genes. The network construction is conceptually straightforward: nodes represent genes …

Gene co-expression networks are increasingly used to explore the system-level functionality
of genes. The network construction is conceptually straightforward: nodes represent genes …

Motivation Weighted gene co-expression network analysis (WGCNA) is frequently used to
identify modules of genes that are co-expressed across many RNA-seq samples. However …

The merging of network theory and microarray data analysis techniques has spawned a new
field: gene coexpression network analysis. While network methods are increasingly used in …

Motivation Co-expression networks are a powerful gene expression analysis method to
study how genes co-express together in clusters with functional coherence that usually …

A correlation network is a network whose adjacency matrix is constructed on the basis of
pairwise correlations between numeric vectors. The numeric vectors may represent …

In scientific fields such as systems biology, evaluation of the relationship between network
members (vertices) is approached using a network structure. In a co-expression network …