计算机科学
网络拓扑
拓扑(电路)
对偶(语法数字)
联想(心理学)
图形
GSM演进的增强数据速率
语义学(计算机科学)
人工智能
理论计算机科学
数据挖掘
机器学习
数学
计算机网络
艺术
哲学
文学类
组合数学
程序设计语言
认识论
作者
Wenjing Yin,Shudong Wang,Sibo Qiao,Yawu Zhao,Wenhao Wu,Shanchen Pang,Zhihan Lv
出处
期刊:IEEE Journal of Biomedical and Health Informatics
[Institute of Electrical and Electronics Engineers]
日期:2023-06-12
卷期号:28 (8): 4421-4431
被引量:2
标识
DOI:10.1109/jbhi.2023.3284851
摘要
There exists growing evidence that circRNAs are concerned with many complex diseases physiological processes and pathogenesis and may serve as critical therapeutic targets. Identifying disease-associated circRNAs through biological experiments is time-consuming, and designing an intelligent, precise calculation model is essential. Recently, many models based on graph technology have been proposed to predict circRNA-disease association. However, most existing methods only capture the neighborhood topology of the association network and ignore the complex semantic information. Therefore, we propose a Dual-view Edge and Topology Hybrid Attention model for predicting CircRNA-Disease Associations (DETHACDA), effectively capturing the neighborhood topology and various semantics of circRNA and disease nodes in a heterogeneous network. The 5-fold cross-validation experiments on circRNADisease indicate that the proposed DETHACDA achieves the area under receiver operating characteristic curve of 0.9882, better than four state-of-the-art calculation methods.
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