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Explainable and programmable hypergraph convolutional network for data fusion
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AI
DOI：10.1016/j.inffus.2023.101950.png)
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Abstract
En 中文
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.
Keywords:
Imaging genetics
Multi-view disease data
Alzheimer's disease
Deep learning
Hypergraph convolutional network