Ricerc@Sapienza

We predict disease-genes relations on the human interactome network using a methodology that jointly learns functional and connectivity patterns surrounding proteins. To exploit at best latent information in the network, we propose an extended version of random walks, named Random Watcher-Walker (RW2), which is shown to perform better than other state-of-the-art algorithms.

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 characterised 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 (RW2), which is shown to

In this paper we propose an extended version of random walks, named Random Watcher-Walker (RW2), to predict disease-genes relations on the Human Interactome network. $RW^2$ is able to learn rich representations of disease genes (or gene products) features by jointly considering functional and connectivity patterns surrounding proteins. Our method successfully compares with the best-known system for disease gene prediction and other state-of-the-art graph-based methods.