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.
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.
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