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

• 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
• 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

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).

心血管疾病（CVD）及其相关的危险因素，例如高血压和血脂异常，由于死亡率和发病率增加以及医疗保健成本上升，构成了严重的公共卫生烧伤。需要大量的流行病学数据来检测许多单个基因和环境对这些特征的小影响。但是，只有通过整合多个流行病学研究来实现强大推断所需的样本量。来自多项研究的信息的有意义的整合需要开发数据本体，这使得以最佳方式整合信息成为可能，以最大程度地提高信息内容，从而最大程度地利用了检测小效应大小的统计能力。数据集成的第二个复合问题是管理此类研究数据的软件应用程序通常是无法使用的，即数据的“孤岛”，并且无法以句法和语义上有意义的方式共享。因此，需要以互操作方式整合整个研究的基础设施，以确保流行病学心血管研究仍然是目前正在进行的生物医学信息革命中的可行和主要参与者。癌症生物医学信息学网格（CABIGTM）正在通过开发能够交换信息或通过使用受控声音在语义上注释的元数据来交换信息或在语法上互操作的软件系统来解决这些问题。我们的总体目标是开发本体，以整合来自多项研究的心血管流行病学数据。具体而言，我们提出了三个目标：首先，为三个不同的多中心流行病学研究中的每一个都开发了心血管数据本体和声乐，这些研究促进了各种表型的研究和数据挖掘的数据整合。其次，采用技术基础架构，利用模型驱动体系结构（MDA）和CABIGTM工具来利用心血管数据本体和词汇，以促进心血管数据集的集成和宽度共享。第三，通过与已建立的心血管研究网格（CVRG）的合作，促进了跨临床，翻译和流行病学领域的研究社区之间的无缝数据共享和促进宽度数据传播。