NEDD: a network embedding based method for predicting drug-disease associations
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METHODOLOGY
Open Access
NEDD: a network embedding based method for predicting drug-disease associations Renyi Zhou1, Zhangli Lu1, Huimin Luo1,2, Ju Xiang1,3, Min Zeng1 and Min Li1* From The 18th Asia Pacific Bioinformatics Conference Seoul, Korea. 18-20 August 2020
* Correspondence: [email protected] 1 Hunan Provincial Key Lab on Bioinformatics, School of Computer Science and Engineering, Central South University, Changsha, China Full list of author information is available at the end of the article
Abstract Background: Drug 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. Results: In 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. Conclusions: The 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. Keywords: Drug repositioning, Heterogeneous network, Network embedding, Meta path
Background Drug discovery is known for a large amount of money and time it consumes and the high risk it takes [1]. The investments grow continuously in recent years, but the total number of approved drugs remains constant [2]. Therefore, drug repositioning has become a popular approach to save cost by finding novel indications for approved drugs. Since these commercialized drugs have passed various clinical tests, it would save © The Author(s). 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulati
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