Relating diseases based on disease module theory

Abstract

Understanding disease-disease associations can not only help us gain deeper insights into complex diseases, but also lead to improvements in disease diagnosis, drug repositioning and new drug development. Due to the growing body of high-throughput biological data, a number of methods have been proposed for the computation of similarity among diseases during past decades. Recently, the disease module theory has been presented, which states that disease-related genes or proteins tend to interact with each other in the same neighborhood of protein-protein interaction network. In this study, we propose a new method called ModuleSim to measure associations between diseases by using disease-gene association data and protein-protein interaction network data based on disease module theory. By considering the interactions between disease modules and each module’s modularity, ModuleSim outperforms other four popular methods for predicting disease-disease similarity.