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High-Dimensional Data Issues in Aging Research

衰老研究中的高维数据问题

基本信息

批准号：
8437205
负责人：
Bin Nan
金额：
$ 25.57万
依托单位：
UNIVERSITY OF MICHIGAN AT ANN ARBOR
依托单位国家：
美国
项目类别：
财政年份：
2010
资助国家：
美国
起止时间：
2010-04-15 至 2015-03-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8437205
关键词：
Address Age related macular degeneration Aging Algorithms Alzheimer&apos s Disease Amyloid deposition Biomedical Research Brain Brain imaging Cations Collaborations Computer software DNA Methylation Data Data Analyses Dementia Dependence Development Dimensions Disease Epigenetic Process Genetic Genome Genomics Goals Group Structure Health Sciences Image Impaired cognition Individual Inequality Linear Models Linear Regressions Literature Maps Measures Methodology Methods Michigan Modeling Motor Nerve Degeneration Neurodegenerative Disorders Parkinson Disease Patients Performance Pittsburgh Compound-B Positron-Emission Tomography Principal Component Analysis Procedures Property Public Health Radio Research Research Personnel Sampling Severities Signal Transduction Staging Structure Testing Tracer Universities age related aging population base case control cognitive function cohort computer program disease diagnosis epigenome follow-up gene discovery genome wide association study health science research interest markov model novel programs public health relevance research and development response simulation statistical center statistics theories user-friendly

项目摘要

DESCRIPTION (provided by applicant): The broad objectives of this research are developments of regularization methods for high-dimensional data that arise commonly in biomedical studies, particularly studies in genomics, epigenetics, and brain imaging. The specific aims in this proposal are motivated by problems arising in studies of neurodegenerative disorders in an aging population including epigenetic and PiB PET studies for patients with Alzheimer's disease, PET studies of dementia in patients with Parkinson's disease, and the genome-wise association study for the age- related macular degeneration. They include: 1. Developing effective variable selection methods in high- dimensional regression models with grouped structures. We will consider the regularization with a novel convex penalty in multiple linear regression with high-dimensional grouped covariates, generalized linear models with high-dimensional grouped covariates, and multivariate linear regression with both high- dimensional grouped response variables and high-dimensional grouped covariates. Fast algorithms will be developed, theoretical properties such as oracle inequalities will be examined, and finite sample performance will be evaluated through extensive simulations. 2. Developing and evaluating regularization methods for disease diagnosis with functional data. We consider functional linear regression models and functional principal component analysis for imaging data to achieve sparse group effects. Both theoretical and numerical performance of the proposed methods will be examined. 3. Developing new multiple testing methodologies for dependent tests. We propose to use the hidden Markov models, either non-homogeneous or group homogeneous, to characterize the dependence structure among the multiple tests, and develop procedures with enhanced power and correctly controlled false discovery rate. We will examine the asymptotic optimality and numerical performance of the proposed methods. 4. Developing user-friendly computing programs systematically for the proposed statistical methodologies and disseminating them to health sciences researchers.

描述（由申请人提供）：本研究的主要目标是开发生物医学研究中常见的高维数据正则化方法，特别是基因组学、表观遗传学和脑成像研究。该提案的具体目标是由老龄化人群神经退行性疾病研究中出现的问题所激发，包括阿尔茨海默病患者的表观遗传学和 PiB PET 研究、帕金森病患者痴呆的 PET 研究以及年龄相关性黄斑变性的基因组关联研究。它们包括： 1. 在具有分组结构的高维回归模型中开发有效的变量选择方法。我们将考虑在具有高维分组协变量的多元线性回归、具有高维分组协变量的广义线性模型以及具有高维分组响应变量和高维分组协变量的多元线性回归中使用新颖的凸罚分进行正则化。将开发快速算法，检查预言不等式等理论属性，并通过广泛的模拟评估有限样本的性能。 2. 开发和评估利用功能数据进行疾病诊断的正则化方法。我们考虑对成像数据进行函数线性回归模型和函数主成分分析以实现稀疏组效应。将检查所提出方法的理论和数值性能。 3. 为相关测试开发新的多重测试方法。我们建议使用隐马尔可夫模型（非同质或组同质）来表征多个测试之间的依赖结构，并开发具有增强功能和正确控制错误发现率的程序。我们将检查所提出方法的渐近最优性和数值性能。 4. 为拟议的统计方法系统地开发用户友好的计算程序，并将其传播给健康科学研究人员。