Before revision:  Our method distinguishes between links that are common to all networks, links that are specific to only one of the compared networks, and links that are different in that their sign changes between networks. Importantly, we developed a statistical framework to normalize between these different categories of common or changed networklinks. Our approach also allows us to categorize nodes as dominated by common, specific, or differentiated links. We demonstrate the usage of our new method by presenting the analysis of a dataset consisting of different types of cancers. We revealed common , specific, and differentiated links between cancer types and show that each category of links is enriched for genes with distinct Gene Ontology groups. This suggests that our method can detect functional differences. CoDiNA has been implemented in an R package that is available from CRAN .
After revision:  Our method distinguishes between links that are common , specific or different to the networks. Applying CoDiNA to a neurogenesis study identified genes for neuron differentiation. Experimentally overexpressing one candidate resulted in significant disturbance in the underlying neurogenesis' gene regulatory network. We compared data from adults and children with active tuberculosis to test for signatures of HIV. We also identified common and distinct network features for particular cancer types with CoDiNA. These studies show that CoDiNA successfully detects genes associated with the diseases .