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. RW2 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. We perform sensitivity analysis and apply perturbations to ensure robustness. Differently from previous studies, our results demonstrate that connectivity alone is not sufficient to classify disease-related genes.
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