According to the disease module hypothesis the cellular components associated with a disease segregate in the same neighborhood of the human interactome, the map of biologically relevant molecular interactions. Yet, given the incompleteness of the interactome and the limited knowledge of disease-associated genes, it is not obvious if the available data has sufficient coverage to map out modules associated with each disease. Here we derive mathematical conditions for the identifiability of disease modules and show that the network-based location of each disease module determines its pathobiological relationship to other diseases. For example, diseases with overlapping network modules show significant co-expression patterns, symptom similarity, and comorbidity, while diseases residing in separated network neighborhoods are clinically distinct. These tools represent an interactome-based platform to predict molecular commonalities between clinically related diseases, even if they do not share disease genes.
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