Fig. 4. Network-based Model of Disease-Disease Relationship.

a, To illustrate the uncovered network-based relationship between diseases, we place each disease in a 3D disease space, such that their physical distance to other diseases is proportional to 〈d_AB〉 predicted by the interactome-based analysis. Diseases whose modules (spheres) overlap are predicted to have common molecular underpinnings. The colors capture several broad disease classes, indicating that typically diseases of the same class are located close to each other. There are exceptions, such as cerebrovascular disease, which is separated from other cardiovascular diseases, suggesting distinct molecular roots. b-g, Biological similarity shown separately for the predicted overlapping and non-overlapping disease pairs (see Fig. 3d-i for interpretation). Error bars indicate the standard error of the mean. Gray lines show random expectation, either for random protein pairs (b-e,h-k) or for a random disease pair (f,g,l,m), p−values denote the significance of the difference of the means according to a Mann-Whitney U test. h-m, Biological similarity for disease pairs that do not share genes (control set). n, Three overlapping disease pairs in the disease space. Coronary artery diseases and atherosclerosis, as well as hepatic cirrhosis and biliary tract diseases, are diseases with common classification, hence their disease modules overlap. Our methodology also predicts several overlapping disease modules of apparently unrelated disease pairs (Table S1), illustrated through asthma and celiac disease. o, A network- level map of the overlapping asthma-celiac disease network-neighborhood, with yellow we also show the IgA production pathway that plays a biological role in both diseases. We show the names of genes that are either shared by the two diseases or by the pathway, or interact across the modules.