Diseases are often complex, caused by a combination of several factors including genetic, environmental and lifestyle factors. The complexity makes it more challenging to uncover the pathomechanisms underlying genotype-phenotype relationships. Cellular networks offer a simple framework to represent the highly interlinked cellular systems, by reducing cellular components, such as metabolites, proteins, DNA molecules or RNA molecules, to nodes and physical, biochemical or functional interactions to links between them. Diseases can be viewed as perturbations of these cellular networks, that lead to faulty physiological functions. Different diseases can have common deregulated molecular pathways, represented in the network as an overlap of subnetworks that are affected in each disease, particularly if they partially share phenotypes. The discovery of genes associated with multiple diseases is especially interesting because it can shed light on the molecular mechanisms implicated in the commonly affected physiological functions and provide new polyvalent therapeutic targets. This dissertation builds upon a previously developed network-based method, called double specificbetweenness (S2B) method, to prioritize nodes with a higher probability of being simultaneously associated with two phenotypically similar diseases. The method was developed to use undirected networks of physical interactions between proteins and extract a network property, a modified version of betweenness centrality, to prioritize proteins specifically connected with two different diseases. The method was tested with artificial disease network modules and applied to two fatal motor neuron diseases: Amyotrophic Lateral Sclerosis and Spinal Muscular Atrophy. The present work aims to expand the S2B method enabling the analysis of networks with directed interactions. This expansion allows the analysis of signaling and transcriptional regulatory networks, providing new regulatory information that can't be captured with protein-protein interactions, contributing to richer mechanistic hypothesis to explain the common physiological deficiencies. The new extended version of the method was tested with several types of directed artificial disease modules, proving to be able to efficiently predict the network overlap between them and offer new insights into the role of the predicted candidates in the network. The directed S2B was also applied to the same motor neuron disease pair, demonstrating its ability to retrieve novel disease genes associated with regulatory mechanisms dysregulated in motor neuron degeneration.