Alzheimer's Disease Risk Prediction and Pathogeny Extraction Using Fuzzy Graph Evolutionary Generative Adversarial...

Area of Science:

• Computational neuroscience
• Artificial intelligence in medicine
• Bioinformatics

Background:

• Accurate Alzheimer's disease (AD) risk prediction is crucial for clinical management.
• The ambiguity in disease data impedes a comprehensive understanding of AD pathogenesis and limits predictive model efficacy.

Purpose of the Study:

• To develop an advanced model for Alzheimer's disease (AD) risk prediction by integrating fuzzy graph theory and deep learning.
• To explore staged evolutionary patterns of AD and extract pathogenetic insights for early intervention.

Main Methods:

• Utilized fuzzy graphs to quantify interpathogeny associations via fuzzy memberships.
• Developed a fuzzy entropy propagation model to describe AD deterioration as fuzzy entropy spread.
• Introduced a fuzzy graph evolutionary generative adversarial network (FGE-GAN) with fuzzy graph convolution (FGC) layers for risk prediction and pathogeny extraction.

Main Results:

• The FGE-GAN model demonstrated superior performance in disease risk prediction compared to existing state-of-the-art methods.
• Experiments on brain disease datasets validated the model's effectiveness.
• Extracted multiomics pathogenetic factors offered valuable insights for early AD intervention.

Conclusions:

• The proposed FGE-GAN provides an interpretable and effective approach for Alzheimer's disease (AD) risk prediction.
• The integration of fuzzy graph modeling and deep learning advances the understanding of AD pathogenesis.
• The model's ability to extract pathogenetic insights supports the development of targeted early intervention strategies.