Abstract
BackgroundDrug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. Many network-based approaches have been proposed for drug repositioning based on similarity networks, which integrate multiple sources of drugs and diseases. However, these methods may simply view nodes as the same-typed and neglect the semantic meanings of different meta-paths in the heterogeneous network. Therefore, it is urgent to develop a rational method to infer new indications for approved drugs.ResultsIn this study, we proposed a novel methodology named HeteSim_DrugDisease (HSDD) for the prediction of drug repositioning. Firstly, we build the drug-drug similarity network and disease-disease similarity network by integrating the information of drugs and diseases. Secondly, a drug-disease heterogeneous network is constructed, which combines the drug similarity network, disease similarity network as well as the known drug-disease association network. Finally, HSDD predicts novel drug-disease associations based on the HeteSim scores of different meta-paths. The experimental results show that HSDD performs significantly better than the existing state-of-the-art approaches. HSDD achieves an AUC score of 0.8994 in the leave-one-out cross validation experiment. Moreover, case studies for selected drugs further illustrate the practical usefulness of HSDD.ConclusionsHSDD can be an effective and feasible way to infer the associations between drugs and diseases using on meta-path-based semantic network analysis.
Highlights
Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era
We proposed a novel method called HeteSim_DrugDisease (HSDD) based on HeteSim scores to measure the associations of drugs and diseases
We conduct case study to verify the effectiveness of HSDD in inferring drug-disease associations
Summary
Drug repositioning is a promising and efficient way to discover new indications for existing drugs, which holds the great potential for precision medicine in the post-genomic era. The cost for developing a new drug is about $1.8 billion dollars, and the developing time is about 15 years [4] To overcome these problems, researchers and pharmaceutical enterprises have begun to pay their attentions to finding new medical indications from those approved drugs [5]. With the generation of large-scale genomic, transcriptomic and proteomic data, it has become a feasible way to predict new drug-disease associations based on computational models [12]. These methods can be mainly divided into three catalogues: machine learning-based approaches, network-based approaches and text mining and semantic inference approaches [13].
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