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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：
10296695
负责人：
ANITA L DESTEFANO
金额：
$ 61.69万
依托单位：
BOSTON UNIVERSITY MEDICAL CAMPUS
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 2021-12-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10296695
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 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 genetic risk factor high risk innovation insight learning network machine learning algorithm machine learning method 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.

项目总结/文摘