Using a human disease network for augmenting prior knowledge about diseases

The cellular metabolism of a living organism is among the most complex systems that man is currently trying to understand. Part of it is described by so-called protein-protein interaction (PPI) networks, and much effort is spent on analyzing these networks. Recently, there has been much interest in predicting involvement of network nodes (in this case, proteins) in different diseases. Many approaches to this problem exist. We categorize the previous studies into Individual and Network approaches. While the Individual approach focuses on one specific disease without considering its relationship with other diseases, the Network approach considers also these relationships. In this paper, we construct a Human Disease Network (HDN), using a novel approach for discovering relationships among different diseases. We built the HDN for 20 different diseases based on functional and structural information available in the PPI network. We showed that the proposed HDN is biologically meaningful and is capable of augmenting the initial prior knowledge of different diseases by sharing information across highly-related diseases. Furthermore, comparing to previous Individual and Network approaches, our proposed HDN increases the accuracy of predictive models and discovers more and still informative relationships among different diseases, respectively.status: publishe

Using a human disease network for augmenting prior knowledge about diseases

Algorithms and the Foundations of Software technolog