Alzheimer's disease (AD) is a critical national concern, affecting 5.8 million people and costing more than 250 billion annually. However, there is no available cure. Thus, effective strategies are in urgent need to discover AD biomarkers for disease early detection and drug development. In this review, we study AD from a biomedical data scientist perspective to discuss the four fundamental components in AD research: genetics (G), molecular multiomics (M), multimodal imaging biomarkers (B), and clinical outcomes (O) (collectively referred to as the GMBO framework). We provide a comprehensive review of common statistical and informatics methodologies for each component within the GMBO framework, accompanied by the major findings from landmark AD studies. Our review highlights the potential of multimodal biobank data in addressing key challenges in AD, such as early diagnosis, disease heterogeneity, and therapeutic development. We identify major hurdles in AD research, including data scarcity and complexity, and advocate for enhanced collaboration, data harmonization, and advanced modeling techniques. This review aims to be an essential guide for understanding current biomedical data science strategies in AD research, emphasizing the need for integrated, multidisciplinary approaches to advance our understanding and management of AD.


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