Fig. 6.

Examples of experimentally validated network-guided predictions of genes underlying outcomes of human disease. (a) shows a network of transcription factors which regulate mesenchymal transformation, as predicted from a glioma-specific regulatory network inferred from DNA microarray datasets [64]. Activation of transcription factors C/EBPβ and STAT3 correlates with aggressive gliomas and poor clinical outcome, as shown in the Kaplan-Meier plot at the right (Figure adapted with permission from Macmillan Publishers Ltd: [64], 2010). (b) Modularity of the human protein interactome can be used as an indicator of breast cancer prognosis. Taylor and colleagues calculated a subnetwork of hubs with either low or high correlation of co-expression with their interaction partners, termed intermodular (red) and intramodular (blue) hubs, respectively [93]. Patients with hubs that had highly altered correlation of co-expression had a high probability of poor prognosis, as plotted at right (Figure adapted with permission from Macmillan Publishers Ltd: [93], 2009).