Graph Convolutional Autoencoder and Fully-Connected Autoencoder with Attention Mechanism Based Method for Predicting Drug-Disease Associations

自编码 药品 计算机科学 图形 疾病 机制(生物学) 深度学习 节点(物理) 人工智能 机器学习 数据挖掘 理论计算机科学 医学 药理学 哲学 结构工程 认识论 病理 工程类
作者
Ping Xuan,Ling Gao,Nan Sheng,Tiangang Zhang,Toshiya Nakaguchi
出处
期刊:IEEE Journal of Biomedical and Health Informatics [Institute of Electrical and Electronics Engineers]
卷期号:25 (5): 1793-1804 被引量:49
标识
DOI:10.1109/jbhi.2020.3039502
摘要

Predicting novel uses for approved drugs helps in reducing the costs of drug development and facilitates the development process. Most of previous methods focused on the multi-source data related to drugs and diseases to predict the candidate associations between drugs and diseases. There are multiple kinds of similarities between drugs, and these similarities reflect how similar two drugs are from the different views, whereas most of the previous methods failed to deeply integrate these similarities. In addition, the topology structures of the multiple drug-disease heterogeneous networks constructed by using the different kinds of drug similarities are not fully exploited. We therefore propose GFPred, a method based on a graph convolutional autoencoder and a fully-connected autoencoder with an attention mechanism, to predict drug-related diseases. GFPred integrates drug-disease associations, disease similarities, three kinds of drug similarities and attributes of the drug nodes. Three drug-disease heterogeneous networks are constructed based on the different kinds of drug similarities. We construct a graph convolutional autoencoder module, and integrate the attributes of the drug and disease nodes in each network to learn the topology representations of each drug node and disease node. As the different kinds of drug attributes contribute differently to the prediction of drug-disease associations, we construct an attribute-level attention mechanism. A fully-connected autoencoder module is established to learn the attribute representations of the drug and disease nodes. Finally, the original features of the drug-disease node pairs are also important auxiliary information for their association prediction. A combined strategy based on a convolutional neural network is proposed to fully integrate the topology representations, the attribute representations, and the original features of the drug-disease pairs. The ablation studies showed the contributions of data related to three types of drug attributes. Comparison with other methods confirmed that GFPred achieved better performance than several state-of-the-art prediction methods. In particular, case studies confirmed that GFPred is able to retrieve more actual drug-disease associations in the top k part of the prediction results. It is helpful for biologists to discover real associations by wet-lab experiments.
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