计算机科学
聚类分析
光谱聚类
异构网络
数据挖掘
相似性(几何)
嵌入
人工智能
无线网络
电信
图像(数学)
无线
作者
Aoxing Li,Xiang Lin,Haiping Yu
标识
DOI:10.1109/bibm52615.2021.9669773
摘要
Although much progress has been made in new drug development, it is still a costly, complicated and less efficient process. Therefore, drug repositioning studies are highly desirable. So far, many methods based on known drug-target interactions (DTIs) have been designed to detect potential DTIs, but there are many challenges for improving the performance of prediction DTIs. This paper proposes a new method (CaGCN-DTI) for DTIs prediction, which uses a heterogeneous network that incorporates a wide variety of biological data (drug, target, disease, and side effect) to discover potential interactions between drugs and targets. First, the drug-protein similarity network is preprocessed by Spectral clustering, then the graph convolutional network (GCN) combined with attention mechanism and random walk with restart (RWR) is used to aggregate message transmission to the network. Finally, the embedding vectors are used to discover potential DTIs by matrix decomposition. Experiments are performed based on ten-fold cross-validation, and the results show that CaGCN-DTI outperforms other previous methods in terms of AUC and AUPR.
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