Convolutional Neural Network for Disease Prediction, Biomarker Discovery, and Validation in Alzheimer's Disease

Abstract/Summary Alzheimer’s disease (AD) is a devastating neurodegenerative disorder affecting millions globally, yet it remains without a cure and with limited therapeutic options. Early diagnosis and risk prediction through genetic and clinical biomarkers are critical for timely interventions. Our parent R15 project (R15AG083618-01A1, 06/01/2024-05/31/2027), titled Convolutional Neural Network for Disease Prediction, Biomarker Discovery, and Validation in Alzheimer’s Disease, focuses on developing convolutional neural network (CNN)-based models to predict AD risk and identify genetic biomarkers from large-scale genetic data while training undergraduate students in artificial intelligence (AI) and computational genomics. However, the increasing complexity and volume of multimodal data, including genomics, clinical, and MRI, now exceed our current computational infrastructure, limiting model scalability, reproducibility, and student training opportunities. In response to NOSI NOT-OD-24-078, we propose a revision to transition critical components of our pipeline, data preprocessing, model training, interpretation, and visualization, to a secure, scalable, cloud-based platform using Google Cloud Platform (GCP). This revision builds on our established pipelines and prior success with major data repositories such as NIH dbGaP, NIAGADS, and ADNI. We have demonstrated that machine learning (ML) models using genetic data outperform traditional statistical models and have experience