CPST-GAN: Conditional Probabilistic State Transition Generative Adversarial Network With the Biomedical Large Foundation Models.

Abstract

The risk prediction of Alzheimer's disease (AD) is crucial for its early prevention and treatment. However, current risk prediction methods face challenges in effectively extracting and fusing multiomics features, particularly overlooking the multilevel evolutionary mechanisms of AD. This article combines biomedical large foundation models with the conditional generative adversarial network (GAN) to mine the evolutionary patterns of AD by considering the regulatory effect of genes on brain lesions. Specifically, we first use biomedical large foundation models to effectively construct high-quality imaging genetic features. Next, a conditional probabilistic state transition mathematical model is constructed to describe AD progression as state transitions of brain regions under genetic regulations. Based on the mathematical model, a conditional probabilistic state transition GAN (CPST-GAN) is proposed. This algorithm can mine the dynamic evolutionary patterns of AD by fusing brain imaging and genetic features to achieve risk prediction of AD. Finally, experiments on the public imaging genetics datasets validate the effectiveness and superiority of CPST-GAN in evolutionary pattern mining and risk prediction of AD. This article not only provides a reliable intelligence algorithm for early intervention of AD but also offers new insights for future research on AD pathogenesis. The code has been published at github.com/fmri123456/CPST-GAN.