Data Management and Analysis Core

数据管理与分析核心

• 批准号: 10622448
• 负责人: Howard H Chang
• 金额: $ 43.58万
• 依托单位: EMORY UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-15 至 2027-02-28
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10622448
• 关键词: Address; Area; Asia; Bayesian Modeling; Bioinformatics; Biological Markers; Biometry; Blood; Cardiovascular Diseases; Caring; Chronic Disease; Clinical; Collaborations; Collection; Communication; Complex; Computational Biology; Data; Data Analyses; Data Analytics; Data Science; Data Set; Databases; Dedications; Detection; Development; Documentation; Ecological momentary assessment; Engineering; Ensure; Environment; Environmental Exposure; Epigenetic Process; Equation; Evaluation; Funding; Goals; Health; Human Resources; India; Infrastructure; Institution; International; Lead; Longitudinal Surveys; Machine Learning; Measurement; Methodology; Methods; Modeling; Monitor; National Heart, Lung, and Blood Institute; Pathway interactions; Persons; Phenotype; Play; Postdoctoral Fellow; Procedures; Program Research Project Grants; Protocols documentation; Public Health; Reproducibility; Reproducibility of Results; Research; Research Design; Research Methodology; Research Personnel; Research Project Grants; Resources; Risk Reduction; Role; Sampling; Secure; Source; South Asian; Techniques; Technology; Training Programs; Universities; Urine; Work; analytical method; analytical tool; biobank; cardiometabolic risk; cardiovascular disorder epidemiology; cardiovascular disorder prevention; cardiovascular disorder risk; cohort; complex data; data cleaning; data complexity; data management; data sharing; data streams; data visualization; data-driven model; digital; disease diagnostic; disease phenotype; disorder control; disorder prevention; diverse data; epidemiological model; epigenomics; experience; follow-up; graduate student; heterogenous data; high dimensionality; indexing; innovation; learning strategy; longitudinal analysis; machine learning method; machine learning prediction; metabolomics; model development; multidimensional data; multiple omics; predictive modeling; sensor; service programs; statistical and machine learning; structured data; synergism; wearable device; Address; Area; Asia; Bayesian Modeling; Bioinformatics; Biological Markers; Biometry; Blood; Cardiovascular Diseases; Caring; Chronic Disease; Clinical; Collaborations; Collection; Communication; Complex; Computational Biology; Data; Data Analyses; Data Analytics; Data Science; Data Set; Databases; Dedications; Detection; Development; Documentation; Ecological momentary assessment; Engineering; Ensure; Environment; Environmental Exposure; Epigenetic Process; Equation; Evaluation; Funding; Goals; Health; Human Resources; India; Infrastructure; Institution; International; Lead; Longitudinal Surveys; Machine Learning; Measurement; Methodology; Methods; Modeling; Monitor; National Heart, Lung, and Blood Institute; Pathway interactions; Persons; Phenotype; Play; Postdoctoral Fellow; Procedures; Program Research Project Grants; Protocols documentation; Public Health; Reproducibility; Reproducibility of Results; Research; Research Design; Research Methodology; Research Personnel; Research Project Grants; Resources; Risk Reduction; Role; Sampling; Secure; Source; South Asian; Techniques; Technology; Training Programs; Universities; Urine; Work; analytical method; analytical tool; biobank; cardiometabolic risk; cardiovascular disorder epidemiology; cardiovascular disorder prevention; cardiovascular disorder risk; cohort; complex data; data cleaning; data complexity; data management; data sharing; data streams; data visualization; data-driven model; digital; disease diagnostic; disease phenotype; disorder control; disorder prevention; diverse data; epidemiological model; epigenomics; experience; follow-up; graduate student; heterogenous data; high dimensionality; indexing; innovation; learning strategy; longitudinal analysis; machine learning method; machine learning prediction; metabolomics; model development; multidimensional data; multiple omics; predictive modeling; sensor; service programs; statistical and machine learning; structured data; synergism; wearable device

PROJECT SUMMARY / ABSTRACT (Data Management and Analysis Core) The Data Management and Analysis (DMA) Core will provide comprehensive data science support for the Program Project Grant (PPG): Precision Cardiovascular Disease Phenotyping and Pathophysiological Pathways in the CARRS cohort (Precision-CARRS). The overarching goal of the DMA Core is to maintain high scientific rigor in the management and analysis of large and complex datasets. The DMA Core will be based at Emory University and the Center for Chronic Disease Control (CCDC) in India. The DMA Core will include 8 investigators with comprehensive data science expertise and a dedicated team of database engineer, data managers and biostatisticians from both the US and India. The DMA Core will oversee data management, data sharing, and the development of common analytic frameworks to promote integration and synergy across the interconnected aims of the Precision-CARRS Projects. The DMA Core will work closely with the Administrative and Field Coordination (AFC) Core and the CVD Phenotyping Core to ensure high quality data capture and documentation, as well as to provide continuous infrastructure and management for all research data from Precision-CARRS (Aim 1). By leveraging and expanding existing data platforms, we will establish workflows and protocols for consistent data cleaning, harmonization, monitoring and documentation for diverse datasets, including longitudinal surveys, subclinical and clinical phtenotypes, blood and urine biomarker measurements, environmental exposures, and data from high-throughput omics technologies and digital sensors. The DMA Core will also work closely with all Reseach Projects to provide centralized support for data analysis using state-of-the-art biostatistics, bioinformatics and maching learning methods (Aim 2). Specifically, the DMA Core will create synchronized analytic plans for both epidemiologic modeling and predictive equation development across projects. To provide integrated and timely data science support throughout the PPG, two DMA Core investigators (one from Emory and one from CCDC) will serve as liaisons for each Core and Project. Finally, the DMA core will support the development of new and innovative analytic methods motivated by the scientific goals of Precision-CARRS (Aim 3). The DMA Core is particularly poised to contribute in the areas of machine learning for predictive health, latent variable models for handling multivariate and structured data, high-dimensional methods for multi-omics, and integrative analysis of heterogeneous data types. In summary, the DMA Core will be an integral component for Precision-CARRS by offering essential data management and analysis support for PPG research aims, while enhancing the reliability, relevance and reproducility of the PPG’s findings to utlimately sustain the long-term impact and collaborative potential of Precision-CARRS.

项目摘要 /摘要（数据管理和分析核心） 数据管理和分析（DMA）核心将为 计划项目赠款（PPG）：精密心血管疾病型和病理生理学 CARRS队列中的途径（精度卡）。 DMA核心的总体目标是维护 大型和复杂数据集的管理和分析中的高科学严谨性。 DMA核心将是 位于埃默里大学和印度慢性疾病控制中心（CCDC）。 DMA核心将 包括8位具有全面数据科学专业知识的调查员和一个专门的数据库工程师团队 来自美国和印度的数据经理和生物统计学家。 DMA核心将监督数据 管理，数据共享以及开发常见的分析框架，以促进集成和 Precision-Carrs项目的互连目标的协同作用。 DMA核心将密切工作 具有行政和现场协调（AFC）核心和CVD表型核心，以确保高 高质量的数据捕获和文档，以及提供连续的基础架构和管理 来自Precision-Carrs的所有研究数据（AIM 1）。通过利用和扩展现有数据平台，我们将 建立工作流和协议，以一致的数据清洁，协调，监视和文档 对于潜水员数据集，包括纵向调查，亚临床和临床pHTENOTYPES，血液和尿液 来自高通量OMICS技术的生物标志物测量，环境暴露和数据 数字传感器。 DMA核心还将与所有研究项目紧密合作，以提供集中的支持 用于使用最先进的生物统计学，生物信息学和加工学习方法的数据分析（AIM 2）。 具体而言，DMA核心将为流行病学建模和 跨项目的预测方程开发。提供综合而及时的数据科学支持 在整个PPG中，两个DMA核心研究人员（一名来自Emory，一名来自CCDC）将作为联络人 对于每个核心和项目。最后，DMA核心将支持新的创新分析的发展 受到精确卡的科学目标激励的方法（AIM 3）。 DMA核心特别有毒 在机器学习领域的预测健康领域，可用于处理多变量的潜在变量模型 和结构化数据，多摩学的高维方法以及异质数据的综合分析 类型。总而言之，DMA核心将通过提供必需品来成为Precision-Carrs的组成部分 对PPG研究的数据管理和分析支持的目的，同时提高了可靠性，相关性和 在PPG的调查结果中复制，以维持长期影响和协作潜力 精密卡车。