Network-based inferring drug-disease associations from chemical, genomic and phenotype data

With the information of drug, disease phenotype and protein interactions accumulating rapidly, to investigate the relationships between drugs and diseases is a critical importance issue. Until recently, few studies attempt to discover drug-disease associations on a network basis. We integrate drug and phenotype information and protein interaction network together and apply a network propagation approach to infer and evaluate the likelihood of the probability between drug and disease based on gene expression profile. In the experiments, we adopt prostate cancer as our test data. We validate our results to the manually curated associations in Comparative Toxicogenomics Database. Our experimental studies show that our proposed method obtains high specificity and sensitivity (AUC=0.98) and clearly outperforms previous existing methods. Our proposed method discovers potential drug-disease associations that drew the attention of biologists and provides a new perspective for toxicogenomics and drug reposition evaluation.