  Biomedical sciences are increasingly recognising the relevance of gene
co-expression-networks for analysing complex-systems, phenotypes or diseases.
When the goal is investigating complex-phenotypes under varying conditions, it
comes naturally to employ comparative network methods. While approaches for
comparing two networks exist, this is not the case for multiple networks. Here
we present a method for the systematic comparison of an unlimited number of
networks: Co-expression Differential Network Analysis (CoDiNA) for detecting
links and nodes 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.
