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（数据管理与分析核心）