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Statistical Methods for Ultrahigh-dimensional Biomedical Data

超高维生物医学数据的统计方法

基本信息

批准号：
10093056
负责人：
Matias Damian Cattaneo
金额：
$ 29.3万
依托单位：
PRINCETON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2006
资助国家：
美国
起止时间：
2006-02-01 至 2023-01-31
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/10093056
关键词：
Address Alzheimer&apos s Disease Big Data Big Data Methods Biological Biomedical Research Brain Classification Clinical Communication Computer software Cox Models Cox Proportional Hazards Models Data Data Set Databases Dependence Dimensions Disease Disease Progression Evaluation Gene Expression Gene Proteins Genes Genomics Heterogeneity Internet Investigation Learning Linear Models Location Meta-Analysis Methods Molecular Outcome Ownership Patients Polynomial Models Principal Component Analysis Privacy Proteins Proteomics Research Role Statistical Data Interpretation Statistical Methods Tail Techniques Testing Time autism spectrum disorder big biomedical data bioinformatics tool cell type computing resources feature selection genetic information health data high dimensionality high throughput analysis improved machine learning method macrophage model building next generation novel precision medicine predict clinical outcome simulation statistical and machine learning statistics therapeutic target tool transcriptome sequencing treatment effect

项目摘要

This proposal develops novel statistics and machine learning methods for distributed analysis of big data in biomedical studies and precision medicine and for selecting a small group of molecules that are associated with biological and clinical outcomes from high-throughput data such as microarray, proteomic, and next generation sequence from biomedical research, especially for autism studies and Alzheimer’s disease research. It focuses on developing efficient distributed statistical methods for Big Data computing, storage, and communication, and for solving distributed health data collected at different locations that are hard to aggregate in meta-analysis due to privacy and ownership concerns. It develops both computationally and statistically efficient methods and valid statistical tools for exploring heterogeneity of big data in precision medicine, for studying associations of genomics and genetic information with clinical and biological outcomes, and for feature selection and model building in presence of errors-in- variables, endogeneity, and heavy-tail error distributions, and for predicting clinical outcomes and understanding molecular mechanisms. It introduces more robust and powerful statistical tests for selection of significant genes, SNPs, and proteins in presence of dependence of data, valid control of false discovery rate for dependent test statistics, and evaluation of treatment effects on a group of molecules. The strength and weakness of each proposed method will be critically analyzed via theoretical investigations and simulation studies. Related software will be developed for free dissemination. Data sets from ongoing autism research, Alzheimer’s disease, and other biomedical studies will be analyzed by using the newly developed methods and the results will be further biologically confirmed and investigated. The research findings will have strong impact on statistical analysis of high throughput big data for biomedical research and on understanding heterogeneity for precision medicine and molecular mechanisms of autism, Alzheimer’s disease, and other diseases.

该建议为分布式分析开发了新的统计和机器学习方法生物医学研究和精准医学中的大数据，以及选择一小群与来自高通量数据的生物学和临床结果相关的分子例如来自生物医学研究微阵列、蛋白质组和下一代序列，尤其是自闭症研究和阿尔茨海默病研究。它专注于开发用于大数据计算、存储和通信的高效分布式统计方法，用于解决在不同位置收集的难以聚合的分布式健康数据出于隐私和所有权方面的考虑，在荟萃分析中。它在计算和计算方面都得到了发展用于探索大数据异构性的统计高效方法和有效的统计工具精确医学，用于研究基因组学和遗传信息与临床的关系和生物学结果，以及在存在错误的情况下进行特征选择和模型建立。变量、内生性和重尾误差分布，以及用于预测临床结果以及了解分子机制。它引入了更强大的统计功能在存在数据依赖性的情况下选择重要基因、SNPs和蛋白质的测试，有效控制相依试验统计的误发率，并对治疗进行评估对一组分子的影响。每种建议方法的优点和缺点如下通过理论调查和模拟研究进行了批判性分析。相关软件将被为免费传播而开发的。来自正在进行的自闭症研究的数据集，阿尔茨海默病，和其他生物医学研究将通过使用新开发的方法和结果将得到进一步的生物学证实和调查。研究结果将会有对生物医学研究等高通量大数据的统计分析产生强烈影响了解精准医学的异质性和自闭症的分子机制，阿尔茨海默氏症和其他疾病。