Predicting drug-disease associations based on the known association bipartite network

Recent studies show that drug-disease associations provide important information for drug discovery and drug repositioning. Wet experimental identification of drug-disease associations is time-consuming and labor-intensive. Therefore, the development of computational methods that predict drug-disease associations is an urgent task. In this paper, we propose a novel computational method named NTSIM, which only uses known drug-disease associations to predict unobserved associations. First of all, known drug-disease associations are represented as a drug-disease bipartite network, and a novel similarity measure named linear neighborhood similarity (LNS) is proposed to calculate drug-drug similarity and disease-disease similarity based on the bipartite network. Then, we predict unobserved drug-disease associations in the similarity-based graph by using label propagation process. In the computational experiments, this proposed method achieves high-accuracy performances, and outperforms representative state-of-the-art methods: PREDICT, TL-HGBI and LRSSL. Our studies reveal that known drug-disease associations can provide enough information to build the high-accuracy prediction models; linear neighbor similarity (LNS) can lead to better performances than other similarity measures such as Jaccard similarity, Gauss similarity and cosine similarity; the bipartite network-derived features outperform the drug biological features and disease semantic features.