Community Detection in Biological Networks: Lessons from the DREAM 2016 Module Identification Challenge

The 2016 DREAM Disease Module Identification Challenge was developed to systematically assess the state of computational module identification methods on a diverse collection of molecular networks. Six different anonymized networks were presented with the gene names anonymized. The goal was to partition the genes into non-overlapping modules of from 3-100 genes each, based soley on the
patterns of network connectivity. Collections of modules were scored based on the number of modules that were statistically significantly
enriched for a set of trait or disease-related phenotypes, according to a set of previously published GWAS datasets. For the first subchallenge,
gene names were anonymized separately for each network and it asked for modules in each of the six networks considered separately; the second subchallenge used the same identifier across networks and asked for one collection of modules integrating information together from all six networks.