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 combinafions 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 annotafing 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 mofion that can predict the early presence of developing heart disease in fime for therapeufic intervenfion. 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 informafics system that allows clinical informafion 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.

Public Health Relevance

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.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Resource-Related Research Projects (R24)
Project #
Application #
Study Section
Special Emphasis Panel (ZHL1-CSR-Y (O1))
Program Officer
Larkin, Jennie E
Project Start
Project End
Budget Start
Budget End
Support Year
Fiscal Year
Total Cost
Indirect Cost
Johns Hopkins University
Biomedical Engineering
Schools of Engineering
United States
Zip Code
Liu, Bo; Madduri, Ravi K; Sotomayor, Borja et al. (2014) Cloud-based bioinformatics workflow platform for large-scale next-generation sequencing analyses. J Biomed Inform 49:119-33
Concannon, Thomas W; Guise, Jeanne-Marie; Dolor, Rowena J et al. (2014) A national strategy to develop pragmatic clinical trials infrastructure. Clin Transl Sci 7:164-71
Madduri, Ravi K; Sulakhe, Dinanath; Lacinski, Lukasz et al. (2014) Experiences Building Globus Genomics: A Next-Generation Sequencing Analysis Service using Galaxy, Globus, and Amazon Web Services. Concurr Comput 26:2266-2279
Teodoro, George; Pan, Tony; Kurc, Tahsin et al. (2013) Efficient Irregular Wavefront Propagation Algorithms on Hybrid CPU-GPU Machines. Parallel Comput 39:189-211
Post, Andrew R; Kurc, Tahsin; Cholleti, Sharath et al. (2013) The Analytic Information Warehouse (AIW): a platform for analytics using electronic health record data. J Biomed Inform 46:410-24
Steinert-Threlkeld, Shane; Ardekani, Siamak; Mejino, Jose L V et al. (2012) Ontological labels for automated location of anatomical shape differences. J Biomed Inform 45:522-7
Kong, Jun; Cooper, Lee; Moreno, Carlos et al. (2011) In silico analysis of nuclei in glioblastoma using large-scale microscopy images improves prediction of treatment response. Conf Proc IEEE Eng Med Biol Soc 2011:87-90
Foran, David J; Yang, Lin; Chen, Wenjin et al. (2011) ImageMiner: a software system for comparative analysis of tissue microarrays using content-based image retrieval, high-performance computing, and grid technology. J Am Med Inform Assoc 18:403-15
Kong, Jun; Cooper, Lee; Kurc, Tahsin et al. (2011) Towards building computerized image analysis framework for nucleus discrimination in microscopy images of diffuse glioma. Conf Proc IEEE Eng Med Biol Soc 2011:6605-8
Quinn, T A; Granite, S; Allessie, M A et al. (2011) Minimum Information about a Cardiac Electrophysiology Experiment (MICEE): standardised reporting for model reproducibility, interoperability, and data sharing. Prog Biophys Mol Biol 107:4-10

Showing the most recent 10 out of 21 publications