The CardioVascular Research Grid

描述(由申请人提供)： 我们建议建立心血管研究网格(CVRG)。CVRG将为国家心血管研究界提供一个发现、表示、联合、共享和分析多尺度心血管数据的协作环境，从而实现跨学科研究，以确定这些数据中可预测疾病风险、治疗和结果的特征。在这份提案中，我们提出了CVRG的发展计划。目标是：开发心血管数据仓库(CDR)。CDR将是一个可以下载并在本地安装的软件包。它将提供管理转录、蛋白质组、成像和电生理(称为“多尺度”)心血管数据所需的网格启用的软件组件。它将包括将CDR节点链接在一起所需的软件组件，以扩展CVRG，通过社区访问和使用CVRG，提供匿名心血管数据集，支持在国内和国际范围内的合作心血管研究，以开发应用编程接口(API)，通过这些API，可以在CVRG上部署新的启用网格的软件组件，如数据分析工具和数据库，以：a)开发新的算法，用于使用MR和CT成像数据对健康和疾病中的心室形状和运动的差异进行参数表征；B)开发稳健、易于解释的统计学习方法，以便在多尺度心血管数据中发现可预测疾病风险、治疗和结果的特征；以及c)通过研究人员友好的门户网站将这些算法部署在CVRG上，供心血管研究社区使用，以制定有效的资源管理政策，以管理项目开发，确保所有资源软件的广泛传播和支持，并建立CVRG工作组，作为与心血管研究社区的数据管理和分析需求互动和回应的手段，并扩大管理CVRG节点的研究组织集。(摘要结束)。

DESCRIPTION (provided by applicant): We are proposing to establish the Cardiovascular Research Grid (CVRG). The CVRG will provide the national cardiovascular research community a collaborative environment for discovering, representing, federating, sharing and analyzing multi-scale cardiovascular data, thus enabling interdisciplinary research directed at identifying features in these data that are predictive of disease risk, treatment and outcome. In this proposal, we present a plan for development of the CVRG. Goals are: To develop the Cardiovascular Data Repository (CDR). The CDR will be a software package that can be downloaded and installed locally. It will provide the grid-enabled software components needed to manage transcriptional, proteomic, imaging and electrophysiological (referred to as "multi-scale") cardiovascular data. It will include the software components needed for linking CDR nodes together to extend the CVRG To make available, through community access to and use of the CVRG, anonymized cardiovascular data sets supporting collaborative cardiovascular research on a national and international scale To develop Application Programming Interfaces (APIs) by which new grid-enabled software components, such as data analysis tools and databases, may be deployed on the CVRG To: a) develop novel algorithms for parametric characterization of differences in ventricular shape and motion in health versus disease using MR and CT imaging data; b) develop robust, readily interpretable statistical learning methods for discovering features in multi-scale cardiovascular data that are predictive of disease risk, treatment and outcome; and c) deploy these algorithms on the CVRG via researcher-friendly web-portals for use by the cardiovascular research community To set in place effective Resource administrative policies for managing project development, for assuring broad dissemination and support of all Resource software and to establish CVRG Working Groups as a means for interacting with and responding to the data management and analysis needs of the cardiovascular research community and for growing the set of research organizations managing nodes of the CVRG. (End of Abstract).