Leveraging Clinical Data for Phenotyping and Predictive Modelling of Alzheimer’s Disease

利用临床数据进行阿尔茨海默病的表型分析和预测模型

基本信息

批准号：
10535399
负责人：
Alice Summer Tang
金额：
$ 3.85万
依托单位：
UNIVERSITY OF CALIFORNIA, SAN FRANCISCO
依托单位国家：
美国
项目类别：
财政年份：
2022
资助国家：
美国
起止时间：
2022-08-01 至 2026-07-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10535399
关键词：
Address Aging Alzheimer&apos s Disease Alzheimer&apos s disease diagnosis Alzheimer&apos s disease model Alzheimer&apos s disease patient Alzheimer&apos s disease risk Animal Model Big Data Biological California Caregivers Classification Clinical Clinical Data Cluster Analysis Cognitive Complex Computerized Medical Record Data Data Set Databases Dementia Development Diagnosis Dimensions Disease Disease Progression European Genetic Genomics Goals Heterogeneity Histopathology Image Informatics Knowledge Link Memory Mentorship Modality Modeling Molecular Nerve Degeneration Neurodegenerative Disorders Neurofibrillary Tangles Onset of illness Output Pathway interactions Patients Pharmaceutical Preparations Phenotype Physicians Prevalence Research Research Proposals Risk Scientist Senile Plaques Sex Differences Specific qualifier value Stratification Time Training Validation Visualization Work cost data warehouse disease heterogeneity disorder risk disorder subtype experience health data human data improved individual variation insight interdisciplinary approach machine learning method molecular domain molecular imaging molecular phenotype multimodality multiple omics patient stratification personalized medicine personalized therapeutic phenotypic data precision medicine predictive modeling resilience sex tau Proteins transcriptomics unsupervised learning

项目摘要

PROJECT SUMMARY/ABSTRACT Alzheimer’s Disease (AD) is a complex and heterogeneous neurodegenerative disorder, with numerous molecular and phenotypic features (e.g., sex) that have been identified as modifiers of disease risk, resilience, and progression. While single-omic (e.g. genomic or transcriptomics) contributions to the variability observed in AD have been studied, there have not been many integrative approaches to holistically understand precise mechanisms that link molecular pathways with clinical manifestations. With the abundance of longitudinal multi- modal clinical data (e.g., UCSF electronic medical records) and the development of integrative knowledge networks that link known relationships across multi-omic modalities (e.g., Scalable Precision Medicine Oriented Knowledge Engine), there is an untapped opportunity to derive further insights into the disease. I hypothesize that by utilizing integrative knowledge network representations on clinical datasets, I can characterize AD heterogeneity and apply predictive modelling to identify potential clinical and molecular features associated with AD risk, subtypes, and sex-specific differences. In Aim 1, I will characterize Alzheimer’s Disease heterogeneity through association analysis and utilization of unsupervised machine learning approaches. In Aim 2, I will develop predictive modelling approaches for identifying clinical and molecular features associated with AD progression. With this approach, I will aim to elucidate potential disease mechanisms underlying heterogeneous clinical manifestations, allowing for improved patient stratification and personalized therapeutic approaches. To pursue this project, I have the support of my sponsor Dr. Marina Sirota, an expert in integrative computational approaches and machine learning methods on clinical and omics data. I will also receive mentorship and support from my collaborators Dr. Sergio Baranzini, an expert in integrative networks and multi-omics integration, Dr. Kate Rankin, an exceptional and leading expert in neurodegeneration characterization, and Dr. Dena Dubal, an exceptional physician-scientist and expert in neurodegeneration sex-differences and resilience. Through this work, I will develop a variety of expertise across integrative computational and multi-disciplinary approaches that will allow for meaningful contributions to improve AD diagnosis and treatment and ultimately strengthen my training as an aspiring physician-scientist.

项目摘要/摘要阿尔茨海默氏病（AD）是一种复杂而异质的神经退行性疾病，有许多分子和表型特征（例如性别）已被确定为疾病风险，韧性，和进展。而单词（例如基因组或转录组学）对观察到的可变性的贡献广告已经进行了研究，没有很多整体理解精确的综合方法将分子途径与临床表现联系起来的机制。随着纵向多的抽象模态临床数据（例如UCSF电子病历）和综合知识的发展将已知关系跨多摩变方式链接的网络（例如，可扩展的精度药物为导向知识引擎），有一个未开发的机会来获得对疾病的进一步见解。我假设通过在临床数据集上使用综合知识网络表示形式，我可以表征AD异质性并应用预测建模以识别潜在的临床和分子特征与AD风险，亚型和性别特定的差异相关。在AIM 1中，我将描述阿尔茨海默氏病通过关联分析和无监督的机器学习方法的异质性。目标 2，我将开发预测建模方法，以识别与广告进展。通过这种方法，我将旨在阐明潜在的疾病机制异质临床表现，可以改善患者分层和个性化治疗方法。要购买该项目，我得到了赞助商Marina Sirota博士的支持，Marina Sirota博士是综合计算专家有关临床和OMIC数据的方法和机器学习方法。我还将获得精通和支持从我的合作者Sergio Baranzini博士（综合网络和多摩变集成专家）中。凯特·兰金（Kate Rangin 非凡的身体科学家和神经变性性别差异和韧性的专家。通过这个工作，我将在综合计算和多学科方法中发展各种专业知识，将允许有意义的贡献来改善广告诊断和治疗，并最终加强我的作为有抱负的身体科学家培训。