  We predict disease-genes relations on the Human Interactome network using a
methodology that jointly learns functional and connectivity patterns
surrounding proteins. Contrary to other data structures, the Interactome is
characterized by high incompleteness and absence of explicit negative
knowledge, which makes predictive tasks particularly challenging. To exploit at
best latent information in the network, we propose an extended version of
random walks, named Random Watcher-Walker ($RW^2$), which is able to learn rich
representations of disease genes (or gene products) features. Our method
successfully compares with the best known system for disease gene prediction,
and other state-of-the-art graph-based methods. We perform sensitivity analysis
and apply perturbations to ensure robustness. In contrast with previous
studies, our results demonstrate that connectivity alone is not sufficient to
classify disease-related genes.
