Context Perception Attention Generative Adversarial Network with Large Foundation Models for Alzheimer's Disease Risk Prediction.
TL;DR
An integrated framework fusing spatial and temporal information is proposed to improve prediction capability and develops a Context Perception Attention Generative Adversarial Network (CPA-GAN) that leverages adversarial training to mine AD evolutionary patterns, thereby supporting risk prediction and pathogeny extraction.
Abstract
Alzheimer's disease (AD) risk prediction relies on accurately characterizing pathological mechanisms underlying AD progression. However, existing methods struggle with heterogeneous multi-omics data and often fail to capture the spatiotemporal dynamics of the disease, limiting their predictive performance. In this paper, an integrated framework fusing spatial and temporal information is proposed to improve prediction capability. First, brain region-gene directed networks are constructed based on large foundation model-enhanced features. Second, a context perception attention model is designed to characterize topological changes of directed networks during AD progression. Based on this model, we develop a Context Perception Attention Generative Adversarial Network (CPA-GAN) that leverages adversarial training to mine AD evolutionary patterns, thereby supporting risk prediction and pathogeny extraction. Finally, the superiority, effectiveness, and robustness of CPA-GAN are validated by extensive experiments. Overall, this work provides a robust and effective modeling framework tailored for early-stage AD risk prediction.