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Objective: Human-curated disease ontologies are widely used for diagnostic evaluation, treatment and data comparisons over time, and clinical decision support. The classification principles underlying these ontologies are guided by the analysis of observable pathological similarities between disorders, often based on anatomical or histological principles. Although, thanks to recent advances in molecular biology, disease ontologies are slowly changing to integrate the etiological and genetic origins of diseases, nosology still reflects this "reductionist" perspective. Proximity relationships of disease modules (hereafter DMs) in the human interactome network are now increasingly used in diagnostics, to identify pathobiologically similar diseases and to support drug repurposing and discovery. On the other hand, similarity relations induced from structural proximity of DMs also have several limitations, such as incomplete knowledge of disease-gene relationships and reliability of clinical trials to assess their validity. The purpose of the study described in this paper is to shed more light on disease similarities by analyzing the relationship between categorical proximity of diseases in human-curated ontologies and structural proximity of the related DM in the interactome. Method: We propose a methodology (and related algorithms) to automatically induce a hierarchical structure from proximity relations between DMs, and to compare this structure with a human-curated disease taxonomy. Results: We demonstrate that the proposed methodology allows to systematically analyze commonalities and differences among structural and categorical similarity of human diseases, help refine and extend human disease classification systems, and identify promising network areas where new disease-gene interactions can be discovered., Comment: 8 pages, 5 figures

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.

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 ( $RW^{2}$ ), which is shown to perform better than other state-of-the-art algorithms. We also show that performance of $RW^{2}$ and other compared state-of-the-art algorithms is extremely sensitive to the interactome used, and to the adopted disease categorizations, since this influences the ability to capture regularities in presence of sparsity and incompleteness.