期刊
BMC BIOINFORMATICS
卷 21, 期 -, 页码 -出版社
BMC
DOI: 10.1186/s12859-020-03682-4
关键词
Drug repositioning; Heterogeneous network; Network embedding; Meta path
类别
资金
- National Natural Science Foundation of China [61832019]
BackgroundDrug discovery is known for the large amount of money and time it consumes and the high risk it takes. Drug repositioning has, therefore, become a popular approach to save time and cost by finding novel indications for approved drugs. In order to distinguish these novel indications accurately in a great many of latent associations between drugs and diseases, it is necessary to exploit abundant heterogeneous information about drugs and diseases.ResultsIn this article, we propose a meta-path-based computational method called NEDD to predict novel associations between drugs and diseases using heterogeneous information. First, we construct a heterogeneous network as an undirected graph by integrating drug-drug similarity, disease-disease similarity, and known drug-disease associations. NEDD uses meta paths of different lengths to explicitly capture the indirect relationships, or high order proximity, within drugs and diseases, by which the low dimensional representation vectors of drugs and diseases are obtained. NEDD then uses a random forest classifier to predict novel associations between drugs and diseases.ConclusionsThe experiments on a gold standard dataset which contains 1933 validated drug-disease associations show that NEDD produces superior prediction results compared with the state-of-the-art approaches.
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