Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks.
Here we demonstrate the utility of DiME with glioblastoma patient sample gene expression data, from which we infer tightly co-expressed clusters of genes in different tumor grades to highlight genes that underwent differential regulation. The transcription factors that regulate these genes may be key to orchestrating the progression of brain tumors.
A detailed tutorial is provided in this link: https://www.cs.bham.ac.uk/~szh/DiME/ For theoretical background on the DiME algorithm, please refer to my paper here: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0086693 https://github.com/salamandastron/DiME/blob/master/figure.png