DEVELOPMENT OF DATA ONTOLOGIES FOR INTEGRATING MULTI-CENTER CARDIOVASCULAR STUDIE

开发整合多中心心血管研究的数据本体

基本信息

批准号：
7851333
负责人：
RAKESH NAGARAJAN
金额：
$ 47.49万
依托单位：
WASHINGTON UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2009
资助国家：
美国
起止时间：
2009-07-01 至 2011-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/7851333
关键词：
Address Adopted Architecture Belief Bioinformatics Biological Assay Cardiovascular Diseases Cardiovascular system Clinical Collaborations Common Data Element Communities Complex Computer software Computerized Medical Record Controlled Vocabulary Data Data Analyses Data Set Databases Development Dimensions Disease Dyslipidemias Elements Ensure Environment Epidemiologic Studies Epidemiology Equipment Failure Family Study Ferrets Genes Genetic Genotype Goals Health Care Costs Human Hypertension Individual Language Literature Malignant Neoplasms Measurement Measures Metadata Methods Modeling Morbidity - disease rate National Cancer Institute Natural Language Processing Nature Ontology Phenotype Physiological Protocols documentation Public Health Publishing Research Research Infrastructure Research Personnel Resources Risk Factors Sample Size Scientist Solutions Strategic Planning Structure System Technology Time Time Study Vocabulary Work anticancer research biomedical informatics cancer Biomedical Informatics Grid cardiovascular disorder risk data integration data mining data sharing design experience mortality software development software systems tool trait user-friendly

项目摘要

Cardiovascular disease (CVD) and its associated risk factors such as hypertension and dyslipidemia constitute a major public-health burden due to increased mortality and morbidity and rising health care costs. Massive epidemiological data are needed to detect the small effects of many individual genes and the environment on these traits. However, sample sizes needed to make powerful inferences may only be reached by integrating multiple epidemiological studies. Meaningful integration of information from multiple studies requires the development of data ontologies which make it possible to integrate information across studies in an optimum manner so as to maximize the information content and hence the statistical power for detecting small effect sizes. A second compounding problem of data integration is that software applications that manage such study data are typically non-interoperable, i.e. “silos” of data, and are incapable of being shared in a syntactically and semantically meaningful manner. Consequently, an infrastructure that integrates across studies in an interoperable manner is needed to ensure that epidemiological cardiovascular research remains a viable and major player in the biomedical informatics revolution which is currently underway. The cancer Biomedical Informatics Grid (caBIGTM) is addressing these problems in the cancer domain by developing software systems that are able to exchange information or that are syntactically interoperable by accessing metadata that is semantically annotated using controlled vocabularies. Our overarching goal is to develop ontologies for integrating cardiovascular epidemiological data from multiple studies. Specifically, we propose three Aims: First, develop cardiovascular data ontologies and vocabularies for each of three disparate multi-center epidemiological studies that facilitate data integration across the studies and data mining for various phenotypes. Second, adopt a technology infrastructure that leverages the cardiovascular data ontologies and vocabularies using Model Driven Architecture (MDA) and caBIGTM tools to facilitate the integration and widespread sharing of cardiovascular data sets. Third, facilitate seamless data sharing and promote widespread data dissemination among research communities cutting across clinical, translational and epidemiological domains, primarily through collaboration with the established CardioVascular Research Grid (CVRG).

由于死亡率和发病率的增加以及医疗费用的上升，心血管疾病（CVD）及其相关危险因素（如高血压和血脂异常）构成了重大的公共卫生负担，需要大量的流行病学数据来检测许多个体基因和疾病的微小影响。然而，只有通过整合多项流行病学研究才能达到做出有力推论所需的样本量。对来自多项研究的信息进行有意义的整合需要开发数据本体，从而能够整合各个研究中的信息。数据集成的第二个复杂问题是，管理此类研究数据的软件应用程序通常是不可互操作的，即数据“孤岛”。并且无法以句法和语义上有意义的方式进行共享，因此需要一个以可互操作的方式整合跨研究的基础设施，以确保流行病学心血管研究仍然是生物医学信息学革命中可行的主要参与者。目前正在进行的癌症生物医学信息网格（caBIGTM）正在通过开发能够交换信息或通过访问使用受控词汇进行语义注释的元数据来进行语法互操作的软件系统来解决癌症领域的这些问题。具体来说，我们提出了三个目标：首先，为每项研究开发心血管数据本体和词汇表。三项不同的多中心流行病学研究，促进跨研究的数据集成和各种表型的数据挖掘其次，采用利用模型驱动架构 (MDA) 和 caBIGTM 工具的心血管数据本体和词汇表的技术基础设施，以促进集成和分析。第三，主要通过与现有的心血管研究机构合作，促进跨临床、转化和流行病学领域的研究团体之间的无缝数据共享并促进广泛的数据传播。网格（CVRG）。