Statistical and Machine Learning Methods to Address Biomedical Challenges for Integrating Multi-view Data

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Abstract Alzheimer’s disease (AD) is a complex and heterogeneous condition that affects 5.8 million adults 65 years or older in the U.S. AD is the most common cause of dementia and presents a substantial and increasing economic and social burden. Our ability to diagnose and classify AD from cognitive normals (CN), or discriminate among individuals with AD, early mild cognitive impairment [EMCI], or late mild cognitive impairment (LMCI), is essential for the prevention, diagnosis, and treatment of AD. Since individuals with MCI have a high chance of converting to AD, effectively discriminating between those who convert to AD (MCI-C) from those who do not convert (MCI- NC) is important for early diagnosis of AD. The heterogeneity of AD has further motivated attempts to classify distinct subgroups of AD to better inform the underlying physiology. There is evidence to suggest that using data across multiple modalities (e.g. genetics, imaging, metabolomics) has potential to classify AD subgroups better than using single modality. However, most AD studies that have used multimodal data have focused on imaging data or imaging and genetics data only. The purpose of this study is to innovatively apply state-of-the-art Machine learning (ML) and Deep Learning (DL) methods we have developed to integrate genetics, imaging, metabolomics, lipidomics, and phenotypic data– from NIAGADS– to better understand the etiology of AD. Our specific goals are: Aim 1 (a) Identify novel molecular signatures and pathways likely differentiating AD cases from cognitively normal (CN), MCI converters [MCI-C] from MCI non-converters[MCI-NC], using multimodal data; (b) Develop polygenic risk scores (PRS) and other molecular risk scores to identify individuals at a higher risk for developing AD to aid in clinical decision making; Aim 2: (a) Characterize molecular changes in AD progression and in ethnic groups [exploration study] and (b) Identify homogeneous subgroups of AD characterized by subgroup-specific molecules and pathways. Although ML and DL have been successfully used in AD research, their potential have not been fully harnessed. The proposed research is feasible, promising and potentially significant to AD research. We expect to identify i) molecular signatures and pathways conferring risk for, or protection against, AD ii) individuals at a higher risk for developing AD and iii) AD molecular subgroups and subgroup-specific molecular biomarkers and pathways. Ultimately, our findings have the potential to contribute to AD research by furthering our understanding of AD mechanisms, refining personalized care, and enhancing our ability to identify targets for disease treatment.