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.

项目概要/摘要阿尔茨海默氏病 (AD) 是一种复杂且异质性的神经退行性疾病，与多种疾病有关分子和表型特征（例如性别）已被确定为疾病风险、恢复力的修饰因素，而单组学（例如基因组学或转录组学）对观察到的变异性的贡献。 AD已经被研究，但还没有很多综合方法来全面理解精确的将分子途径与临床表现联系起来的机制具有丰富的纵向多因素。模式临床数据（例如 UCSF 电子病历）和综合知识的发展将跨多组学模式的已知关系联系起来的网络（例如，面向可扩展的精准医学）知识引擎），这是一个尚未开发的机会来进一步了解该疾病。我认为通过利用临床数据集上的综合知识网络表示，我可以描述 AD 异质性并应用预测模型来识别潜在的临床和分子特征与 AD 风险、亚型和性别差异相关。在目标 1 中，我将描述阿尔茨海默病的特征。通过关联分析和利用无监督机器学习方法来实现异质性。 2，我将开发预测建模方法来识别与相关的临床和分子特征通过这种方法，我的目标是阐明潜在的疾病机制。异质的临床表现，允许改善患者分层和个性化治疗接近。为了开展这个项目，我得到了我的赞助商 Marina Sirota 博士的支持，她是一位综合计算专家我还将获得有关临床和组学数据的方法和机器学习方法。我的合作者 Sergio Baranzini 博士是综合网络和多组学整合方面的专家，凯特·兰金 (Kate Rankin) 是一位杰出的神经退行性疾病表征领域的领先专家，德纳·杜巴尔 (Dena Dubal) 博士是一位杰出的医师科学家和神经退行性变性差异和恢复力方面的专家。在工作中，我将开发跨综合计算和多学科方法的各种专业知识将为改善 AD 诊断和治疗做出有意义的贡献，并最终加强我的能力作为一名有抱负的医生科学家接受培训。