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协作环境，用于发现，代表，联合，共享和分析多尺度心血管数据，从而实现跨学科研究，旨在确定这些数据中预测疾病风险，治疗和结果的特征。在本提案中，我们提出了CVRG的发展计划。目标是：开发心血管数据库（CDR）。CDR将是一个可以在本地下载和安装的软件包。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化软件组件。它将包括将CDR节点连接在一起以扩展CVRG所需的软件组件。通过社区访问和使用CVRG，提供匿名心血管数据集，支持国家和国际范围的心血管合作研究。开发应用程序编程接口（API），通过该接口，新的网格启用软件组件，如数据分析工具和数据库，可以部署在CVRG上，以：a）使用MR和CT成像数据开发用于健康与疾病中心室形状和运动差异的参数表征的新算法; B）开发用于发现多尺度心血管数据中预测疾病风险、治疗和结局的特征的稳健、易于解释的统计学习方法;以及c）通过研究者友好的门户网站在CVRG上部署这些算法，以供心血管研究团体使用。确保所有资源软件的广泛传播和支持，并建立CVRG工作组，作为与心血管疾病数据管理和分析需求进行互动和响应的手段，研究社区，并增加管理CVRG节点的研究组织的集合。(End抽象）。

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).