A computational drug repositioning model based on hybrid similarity side information powered graph neural network

作     者：Li, Sumin Pan, Xiuqin 

作者机构：Minzu Univ China Sch Informat Engn Beijing 100081 Peoples R China 

出 版 物：《FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE》 (Future Gener Comput Syst)

年 卷 期：2021年第125卷

页      面：24-31页

核心收录：

学科分类：08[工学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

主　　题：Computational drug repositioning Drug-disease association prediction Graph neural networks Side information Dimensionality reduction algorithm 

摘      要：Computational drug repositioning technology aims to rediscover the potential use of drugs already on the market and can significantly accelerate the traditional drug development process, reducing significant drug development costs and drug development instability In this work, in order to capture valid and robust hidden feature representations of drugs and diseases, we introduce a new computational drug relocation model, HSSIGNN, based on hybrid similarity side information powered graph neural network, by drawing on the application of graph neural networks and Side information in recommender systems. Its advantage is to utilize the learning capability of graph neural networks to capture the effective hidden feature representation of drugs and diseases, which is used to infer the probability of whether a drug can treat the disease of interest, as a way to improve the generalization capability of the model. In addition, dimensionality reduction algorithms and side information of drugs and diseases are used to overcome the cold start problem encountered by traditional computational drug relocation models. Finally, the experimental results of the proposed model on two real drug-disease association datasets are analyzed to verify its superiority and effectiveness. Comprehensive experimentations on several real-world datasets show the efficiency of HSSIGNN. (C) 2021 Elsevier B.V. All rights reserved.