Assessing Alzheimer disease risk and heterogeneity using multimodal machine learning approaches

使用多模式机器学习方法评估阿尔茨海默病风险和异质性

基本信息

批准号：
10655876
负责人：
ANITA L DESTEFANO
金额：
$ 64万
依托单位：
UNIV OF MASSACHUSETTS MED SCH WORCESTER
依托单位国家：
美国
项目类别：
财政年份：
2021
资助国家：
美国
起止时间：
2021-09-15 至 2025-08-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10655876
关键词：
Accounting Alzheimer&apos s Disease Alzheimer&apos s disease risk Alzheimer’s disease biomarker Autopsy Biological Markers Blood Brain Cerebrospinal Fluid Characteristics Clinical Cognitive Collection Communities Complex Data Data Storage and Retrieval Dementia Development Diagnosis Disease Elderly Ethnic group European Future Gene Expression Regulation Genes Genetic Genetic Predisposition to Disease Heterogeneity Human Image Impaired cognition Individual Intervention Machine Learning Measures Medicine Methods Modality Modeling Molecular Multiomic Data National Institute on Aging Nature Nerve Degeneration Non-linear Models Ontology Participant Pathogenesis Pattern Persons Plasma Population Heterogeneity Prevention strategy Regulator Genes Resources Sampling Statistical Models Symptoms Therapeutic Therapeutic Clinical Trial Tissues Weight base clinical risk cognitive function combinatorial deep learning disease heterogeneity disease phenotype disorder subtype effective therapy endophenotype ethnic diversity feature selection follow-up gene interaction gene regulatory network genetic risk factor high risk innovation insight learning network machine learning algorithm machine learning framework machine learning method machine learning model multimodal data multimodality neuroimaging phenotypic data risk stratification supervised learning therapeutic target unsupervised learning

项目摘要

PROJECT SUMMARY/ABSTRACT Alzheimer's disease (AD) is the most common form of dementia characterized by progressive loss of cognitive function. Unfortunately, currently there is no effective treatment for AD and clinical interventions of AD have largely failed despite enormous efforts. For the current application, we seek to develop multimodal machine learning models by leveraging the rich collection of AD-related omics data and phenotypical data recently generated from large-scale collaborative projects such as Alzheimer Disease Neuroimaging Initiative (ADNI), Accelerating Medicines Partnership-AD (AMP-AD) and the Alzheimer's Disease Sequencing Project (ADSP). Three aims will be pursued in the current application. Aim 1. We will build an expandable multimodal unsupervised machine learning framework to investigate AD heterogeneity. Given the multifactorial nature of AD, we will perform AD subtyping by harnessing the rich information across multiple spectrum of data. Aim 2. We will build an expandable multimodal supervised machine learning framework to quantify AD risk from longitudinal follow up of cognitively normal elders. The models will be built from genetic susceptibility and gene regulatory information as well as endophenotypes measured when participants were cognitive normal. Aim 3. We will build AD-related gene interaction networks in post-mortem human brain samples. We will examine the association of multiple omics data with AD in brain samples, and build tissue-specific interaction networks to understand potential molecular mechanisms underlying AD pathogenesis. The present application represents an innovative approach to identify individuals at high risk of AD from both clinical and genetic risk factors in ethnically diverse populations. The outlined strategy will provide new insights into the risk stratification and prevention strategies for AD. We also commit to share our methods through GitHub or CRAN for free access across the scientific community.

项目摘要/摘要阿尔茨海默氏病（AD）是最常见的痴呆症形式，其特征是认知的逐渐丧失功能。不幸的是，目前尚无有效的AD和AD临床干预措施尽管付出了巨大的努力，但在很大程度上还是失败了。对于当前应用程序，我们寻求开发多模式机器通过利用广告相关的OMIC数据和表型数据的丰富收集来学习模型由大规模合作项目（例如阿尔茨海默氏病神经影像学计划（ADNI））产生的加速药物合作伙伴关系（AMP-AD）和阿尔茨海默氏病测序项目（ADSP）。当前申请将追求三个目标。目标1。我们将构建可扩展的多模式无监督的机器学习框架，以调查AD异质性。鉴于多因素的性质 AD，我们将通过利用多种数据频谱来利用丰富的信息来执行AD子类型。目标2。我们将构建一个可扩展的多模式监督机学习框架，以量化AD风险认知正常长者的纵向随访。这些模型将由遗传敏感性和基因建立当参与者是认知正常时，测量的调节信息以及内表型。目标3。我们将在验尸后人脑样本中构建与广告相关的基因相互作用网络。我们将检查多个OMIC数据与大脑样本中的AD的关联，并建立组织特异性的相互作用网络了解AD发病机理的潜在分子机制。本申请代表一种创新的方法，可以从临床和遗传危险因素中识别出AD高风险的个体种族多样化的人群。概述的策略将为风险分层提供新的见解和预防AD的策略。我们还致力于通过GitHub或Cran分享我们的方法以免费访问在整个科学界。