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.
Zhao-Xu Xing, Da-Fang Zhang, Kun Xie et al.· IEEE Transactions on Medical...· 0 citations
Predicting the risk of Alzheimer's disease (AD) is fundamental for early-stage intervention. Nevertheless, most methods struggle to extract multi-omics associative patterns due to the limited feature perception and inflexible disease modeling. This paper proposes a novel evolutionary pattern mining framework for precise disease risk prediction. Firstly, large foundational models are employed to automatically construct high-quality features. Second, a perceptual deformable attention mathematical model is proposed, which combines multi-scale sparse attention and deformable attention mechanisms to capture evolutionary patterns of fused multi-omics features. Finally, a Perceptual Deformable Attention Generative Adversarial Network (PDAT-GAN) is developed. PDAT-GAN can precisely simulate the evolutionary procedure of AD using multi-omics data, thereby achieving robust risk prediction and pathogeny extraction for AD. We validate the advanced performance and interpretability of PDAT-GAN on public datasets, underscoring significance of PDAT-GAN in supporting clinical intervention and pathogenetic research. The code of PDAT-GAN can be accessed at: .
Zhao-Xu Xing, Zheng Liu, Da-Fang Zhang et al.· Medical Image Anal.· 0 citations
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