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

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 项目资助(PPG)：精确的心血管疾病表型和病理生理学 CARRS队列中的路径(Precision-CARRS)。DMA核心的首要目标是维护 在管理和分析大型复杂数据集方面具有高度的科学严谨性。DMA核心将是 设在埃默里大学和印度慢性病控制中心(CCDC)。DMA核心将 包括8名具有全面数据科学专业知识的调查人员和一支敬业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、数据共享和开发通用分析框架，以促进集成和 Precision-CARRS项目相互关联的目标之间的协同效应。DMA核心将密切合作 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记录，以及提供持续的基础设施和管理 所有研究数据均来自Precision-CARRS(目标1)。通过利用和扩展现有数据平台，我们将 建立工作流程和协议，以实现一致的数据清理、协调、监控和文档编制 对于不同的数据集，包括纵向调查、亚临床和临床病例类型、血液和尿液 生物标记物测量、环境暴露和来自高通量组学技术和 数字传感器。DMA核心还将与所有研究项目密切合作，提供集中支持 使用最先进的生物统计学、生物信息学和机器学习方法进行数据分析(目标2)。 具体地说，DMA核心将为流行病学建模和 跨项目的预测性公式开发。提供全面、及时的数据科学支持 在整个PPG期间，两名DMA核心调查员(一名来自埃默里，一名来自CCDC)将担任联络人 对于每个核心和项目。最后，DMA核心将支持开发新的和创新的分析 方法以Precision-CARRS的科学目标为动力(目标3)。DMA核心特别准备好 在用于预测健康的机器学习、用于处理多变量的潜变量模型等领域做出贡献 和结构化数据，多组学的高维方法，以及异质数据的综合分析 类型。总而言之，DMA核心将成为Precision-CARRS的一个不可或缺的组件，因为它提供了 为PPG研究目标提供数据管理和分析支持，同时增强可靠性、相关性和 PPG调查结果的重复性，以彻底维持以下项目的长期影响和合作潜力 精度-CARRS。