Disease candidate gene prioritization addresses the association of genes with disease susceptibility. Network-based approaches have successfully exploited the connectivity of biological networks to compute a disease-relatedness score between candidate and known disease genes. Nonetheless, available strategies yield three major concerns: (1) most networks used rely exclusively on curated physical interactions, resulting in poor genome coverage and sparsity issues; (2) devised scores are often local and thus restrict the search to a limited neighborhood around known genes and ignore potentially informative indirect paths; (3) some methods disregard interaction confidence weights which could confer extra reliability. Results: We hypothesized that capturing disease-relatedness at the interactome scale based on weighted gene associations integrated from heterogeneous sources is likely to outperform current methods lacking one of these features and proposed to combine a particular personalized ranking method with data from STRING. Our claim was confirmed in comparative leave-one-out cross-validation case studies assessing the impact of network density and coverage, score globality and confidence weights on the prioritization of candidate genes for 29 diseases. Finally, the proposed method was applied to Parkinson’s disease and proved effective to recover prior knowledge and unravel interesting genes which could be linked to several pathological mechanisms of the disease.