The Cardiovascular Research Grid (CVRG) Project is an R24 resource supporting the informatics needs of the cardiovascular (CV) research community. The CVRG Project has developed and deployed unique core technology for management and analysis of CV data that is being used in a broad range of Driving Biomedical Projects (DBFs). This includes: a) tools for storing and managing different types of biomedical data;b) methods for securing the data;c) tools for querying combinations of these data so that users may mine their data for new knowledge;d) new statistical learning methods for biomarker discovery;e) novel tools that analyze image data on heart shape and motion to discover biomarkers that are indicative of disease;f) tools for managing, analyzing, and annotating ECG data. All of these tools are documented and freely available from the CVRG website and Wiki. In this renewal, we propose a set of new projects that will enhance the capability of our users to explore and analyze their data to understand the cause and treatment of heart disease. Each project is motivated directly by the needs of one or more of our DBFs. Project 1 will develop and apply new algorithms for discovering changes in heart shape and motion that can predict the early presence of developing heart disease in time for therapeutic intervention. Project 2 will create data management systems for storing CV image data collected in large, multi-center clinical research studies, and for performing quality control operations that assure the integrity of that data. Project 3 will develop a complete infrastructure for managing and analyzing ECG data. Project 4 will develop a comprehensive clinical informatics system that allows clinical information to be linked with biomedical data collected from subjects. Project 5 will develop tools by which non-expert users can quickly assemble new procedures for analyzing their data. Project 6 will put in place a project management structure that will assure successful operation of the CVRG.
The Cardiovascular Research Grid (CVRG) Project is a national resource providing the capability to store, manage, and analyze data on the structure and function of the cardiovascular system in health and disease. The CVRG Project has developed and deployed unique technology that is now being used in a broad range of studies. In this renewal, we propose to develop new tools that will enhance the ability of researchers to explore and analyze their data to understand the cause and treatment of heart disease.
|Cambronero, Francis E; Liu, Dandan; Neal, Jacquelyn E et al. (2018) APOE genotype modifies the association between central arterial stiffening and cognition in older adults. Neurobiol Aging 67:120-127|
|Post, Andrew R; Ai, Miao; Kalsanka Pai, Akshatha et al. (2017) Architecting the Data Loading Process for an i2b2 Research Data Warehouse: Full Reload versus Incremental Updating. AMIA Annu Symp Proc 2017:1411-1420|
|Teodoro, George; Kurc, Tahsin; Andrade, Guilherme et al. (2017) Application Performance Analysis and Efficient Execution on Systems with multi-core CPUs, GPUs and MICs: A Case Study with Microscopy Image Analysis. Int J High Perform Comput Appl 31:32-51|
|Post, Andrew R; Pai, Akshatha K; Willard, Richard et al. (2016) Metadata-driven Clinical Data Loading into i2b2 for Clinical and Translational Science Institutes. AMIA Jt Summits Transl Sci Proc 2016:184-93|
|Walker, Mark A; Madduri, Ravi; Rodriguez, Alex et al. (2016) Models and Simulations as a Service: Exploring the Use of Galaxy for Delivering Computational Models. Biophys J 110:1038-43|
|Winslow, Raimond L; Granite, Stephen; Jurado, Christian (2016) WaveformECG: A Platform for Visualizing, Annotating, and Analyzing ECG Data. Comput Sci Eng 18:36-46|
|Ardekani, Siamak; Jain, Saurabh; Sanzi, Alianna et al. (2016) Shape analysis of hypertrophic and hypertensive heart disease using MRI-based 3D surface models of left ventricular geometry. Med Image Anal 29:12-23|
|Trubetskoy, Vassily; Rodriguez, Alex; Dave, Uptal et al. (2015) Consensus Genotyper for Exome Sequencing (CGES): improving the quality of exome variant genotypes. Bioinformatics 31:187-93|
|Bjornsson, Eythor; Gudbjartsson, Daniel F; Helgadottir, Anna et al. (2015) Common sequence variants associated with coronary artery disease correlate with the extent of coronary atherosclerosis. Arterioscler Thromb Vasc Biol 35:1526-31|
|Teodoro, George; Pan, Tony; Kurc, Tahsin et al. (2014) Region Templates: Data Representation and Management for High-Throughput Image Analysis. Parallel Comput 40:589-610|
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