However, most module discovery algorithms were developed for binary association networks where each biological entity is represented by a single node.
A motif-module discovery algorithm, CisModule [ 53], was used to detect motif patterns and the 5 most highly over-represented patterns were found (Table 6).
Often, module-discovery is performed by attempting to maximize a modularity measure, but in principle neither does a modularity measure imply an algorithm for module discovery, nor does a module-discovery algorithm necessitate a measure of modularity.
We would also like to highlight the difference between modularity measures, which quantify the modularity in a network whose modules have already been determined, and module-discovery algorithms, which partition a network into groups of nodes.
Another example of this principle is modules identified using community discovery algorithm [ 38], which resulted in the discovery of new links between Alzheimer's disease and CVDs at the coexpression and coregulation levels [ 39].
Here, the co-expression similarity S sj is the absolute value of the PCC between gene expression profiles × i, ×i (1) S ij = c o r (x i, x j ) Step 2. Module discovery using the weighted GN algorithm We applied the widely used weighted Girvan and Newman (GN) algorithm [ 20], a graph theory method based on edge betweenness algorithm.
Our approach consists of a method for rank-based network construction, a parameter-free graph partitioning algorithm for module discovery and a novel reference network-based metric for module evaluation.
The iBBiG bi-clustering algorithm is optimized for module discovery in sparse noisy binary genomics data and can be used for meta-GSA of multiple genomics datasets, to discover modules: groups of phenotypes whose differential gene expression profiles are enriched in the same gene sets.
In addition, we integrated the SNBuilder and GeNa algorithms for subnetwork extraction and functional module discovery.
