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InfoResearchPeer-reviewed
Alzheimer’s Disease Risk Prediction and Pathogeny Extraction Using Fuzzy Graph Evolutionary Generative Adversarial Network
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Summary
Researchers propose FGE-GAN, a fuzzy graph evolutionary generative adversarial network, to predict Alzheimer's disease risk and extract pathogeny. The method models disease progression as fuzzy entropy spreading through fuzzy graphs, using fuzzy graph convolution layers in the generator. Experiments on multiple brain disease datasets reportedly show it outperforms state-of-the-art methods in risk prediction, and the extracted multiomics pathogenies are said to offer insights for early intervention.