SCH: Novel Multi-View Statistical Machine Learning for Alzheimer's Disease

Alzheimer’s disease (AD) is the most common type of dementia without curative medications. Early detection of AD is thus essential for timely intervention and effective treatment development. Multi-view data provides a transformative approach to enhance the understanding, diagnosis, and prediction of AD. This research aims to develop three novel statistical machine learning methods for AD research using multi-view imaging, genomic, and clinical data, focusing on multi-view brain and genomic network analysis, significant differential feature testing, and diagnosis and survival analysis. The specific aims of this proposal include: 1. Develop a novel high-dimensional multi-view data decomposition based on uncorrelated common and distinctive latent factors (C&DLFs) to construct multi-view networks, with application to comparing brain and genomic networks across AD statuses; 2. Develop an optimal false discovery rate (FDR) control method based on a novel semi-parametric hidden Markov random field for high-dimensional spatial multiple testing, with application to identifying significant brain and genomic differences across AD statuses; 3. Develop a highly accurate deep-learning-based diagnosis and survival framework for high-dimensional tabular data, incorporating feature selection and view ablation to enhance cost-effectiveness and data accessibility in clinical practice; 4. Apply the three proposed methods to four large-scale AD-related datasets and disseminate the methods with an open, efficient software package. The three novel methods in our project will undergo rigorous theoretical and numerical analyses. We will combine the de-identified Alzheimer's Disease Research Centers Datasets stored in the NIAGADS database with these centers' de-identified imaging data stored in the NACC database to conduct this secondary data analysis project.

The research goal is to develop three novel statistical machine learning methods for Alzheimer’s disease (AD) using multi-view imaging, genomic, and clinical data. These methods will focus on multi-view brain and genomic network analysis, significant differential feature testing, and diagnosis and survival analysis. The proposed project will enhance our understanding of neuro-genetic associations in AD and significantly contribute to biomarker discovery, early detection, and improved patient survival in AD.