AbstractDiscovering disease-associated genes (DG) is strategic for understanding pathological mechanisms. DGs form modules in protein interaction networks and diseases with common phenotypes share more DGs or have more closely interacting DGs. This prompted the development of Specific Betweenness (S2B) to find genes associated with two related diseases. S2B prioritizes genes frequently and specifically present in shortest paths linking two disease modules. Top S2B scores identified genes in the overlap of artificial network modules more than 80% of the times, even with incomplete or noisy knowledge. Applied to Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy, S2B candidates were enriched in biological processes previously associated with motor neuron degeneration. Some S2B candidates closely interacted in network cliques, suggesting common molecular mechanisms for the two diseases. S2B is a valuable tool for DG prediction, bringing new insights into pathological mechanisms. More generally, S2B can be applied to infer the overlap between other types of network modules, such as functional modules or context-specific subnetworks. An R package implementing S2B is publicly available at https://github.com/frpinto/S2B.