Computational drug repositioning is nowadays a widely used approach for finding new uses of existing and experimental drugs. Network-based methods, which rely on the functional relationships between drugs, genes, proteins, and diseases, have shown to be effective.
In this study, we implemented a state-of-the-art network-based method, random walk with restart algorithm on a heterogeneous network of drugs and diseases, as an app, namely HDR, in Cytoscape platform for drug repositioning. The task was approached in both drug- and disease-centric views. Besides, it can use both pre-installed and imported networks, thus makes the user flexible in selecting datasets of interest. Besides, predicted and existing associations between drugs and diseases as well as involved pathways/genes/targets can be visualized intuitively. The ability of HDR was also shown by comparing its overall prediction performance with some existing methods and by predicting novel drug-disease associations.