超图
可解释性
计算机科学
深度学习
卷积神经网络
人工智能
图形
信息融合
保险丝(电气)
信息抽取
编码
机器学习
理论计算机科学
模式识别(心理学)
基因
数学
遗传学
离散数学
电气工程
工程类
生物
作者
Xia-an Bi,Sheng Luo,Siyu Jiang,Yu Wang,Zhaoxu Xing,Luyun Xu
标识
DOI:10.1016/j.inffus.2023.101950
摘要
Integrating multi-view information to gain a new understanding of complex disease like Alzheimer’s disease (AD) has great clinical value. Hypergraphs have unique advantages in modeling high-order associations, but current deep learning methods cannot fully utilize the structural information in hypergraphs and have limited application value due to the black-box nature. This paper improves the hypergraph learning in interpretability and programmability. Firstly, we fuse multi-view information by constructing brain region-gene hypergraphs. Secondly, a characteristic information aggregation model is constructed based on hypergraph structure, Finally, a characteristic information aggregation hypergraph convolutional network (CIA-HGCN) is proposed based on the idea of graph neural networks. Evaluated by clinical imaging genetics data, CIA-HGCN obtained accuracy of 88.3% in AD identification task and showed superior performance in characteristic extraction. This paper provides a practical and flexible deep learning method for AD research and clinical applications.
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