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

摘要 阿尔茨海默病（AD）是一种复杂且异质性的病症，影响580万65岁或65岁以下的成年人。 AD是痴呆症的最常见原因，并呈现出实质性和不断增长的经济增长。 社会负担。我们诊断和分类AD与认知正常人（CN）的能力，或区分AD与认知正常人（CN）的能力， AD患者，早期轻度认知障碍[EMCI]或晚期轻度认知障碍（LMCI）， 用于AD的预防、诊断和治疗。由于MCI患者有很高的转换机会， 在他们中间，有一个人，是那些不信主的人，是那些不信主的人，是那些信主的人。 NC）对AD的早期诊断有重要意义。AD的异质性进一步促使人们尝试将其分类为 不同的AD亚组，以更好地了解潜在的生理学。有证据表明，使用数据 跨多种模式（例如遗传学、成像、代谢组学）的分类有可能更好地对AD亚组进行分类 而不是使用单一模式。然而，大多数使用多模态数据的AD研究都集中在成像上， 数据或成像和遗传学数据。 本研究的目的是创新性地应用最先进的机器学习（ML）和深度学习（DL） 我们已经开发出整合遗传学、成像、代谢组学、脂质组学和表型数据的方法， NIAGADS-更好地了解AD的病因。我们的具体目标是：目标1（a）鉴定新的分子 可能区分AD病例与认知正常（CN）、MCI转换者[MCI-C]与 MCI非转换者[MCI-NC]，使用多模态数据;（B）开发多基因风险评分（PRS）和其他分子生物学指标。 风险评分，以识别处于发展AD的较高风险的个体，以帮助临床决策;目的2： (a)表征AD进展和种族组中的分子变化[探索性研究]和（B）鉴定 以亚组特异性分子和途径为特征的AD同质亚组。虽然ML和 DL已成功地应用于AD研究，但其潜力尚未得到充分利用。 该研究具有可行性和应用前景，对AD的研究具有重要意义。我们希望能找出 i）赋予AD风险或针对AD的保护的分子特征和途径ii）处于较高风险的个体 用于开发AD和iii）AD分子亚组和亚组特异性分子生物标志物和途径。 最终，我们的研究结果有可能通过进一步了解AD来促进AD研究 机制，完善个性化护理，提高我们识别疾病治疗目标的能力。

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.