of the network by maximizing the modularity score (21); Secondly, we use the edge switching algorithm (22) to generate 1000 random networks with the same attributes as the protein–protein interaction network and then identify the best partition and corresponding modularity score for each random network; Thirdly, if the modularity score for the interaction network is significantly higher than those for the 1000 random networks (P < 0.05), the interaction network is considered to have a modular organization and is divided into sub-networks (modules) according to the identified best partition. To reveal network modules at different hierarchical levels, we repeat the above three steps iteratively for each sub-network until none of them show a modular organization. Using this method, we identified 987 and 1006 hierarchical modules for the human and mouse protein–protein interaction networks, respectively. Eighty percentage of the modules were enriched for at least one GO term (Fisher’s exact test, FDR < 0.05), suggesting high functional relevance of the network-derived modules. More importantly, network modules without GO enrichment may represent functional categories that are not well captured by existing knowledge. Gene sets defined by these protein interaction network modules have been added to WebGestalt. WebGestalt has also added regulatory modules
