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Cardiovascular (CV) Community Needs

The CardioVascular Research Grid: A Resource Supporting National Collaborations in Heart Research NHLBI R24 HL085343 Stephen J. Granite, MS, MBA The Johns Hopkins University Institute for Computational Medicine. Cardiovascular (CV) Community Needs.

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Cardiovascular (CV) Community Needs

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  1. The CardioVascular Research Grid:A Resource Supporting National Collaborations in Heart ResearchNHLBI R24 HL085343Stephen J. Granite, MS, MBAThe Johns Hopkins UniversityInstitute for Computational Medicine

  2. Cardiovascular (CV) Community Needs • Describe both CV data and data collection methods • CV-specific ontologies • Semantic description facilitates re-use of data • Standards-based representation and management of multi-scale CV data • There are unique CV data types for which no standards exist • Time-evolving CV Electrophysiology (EP), electrocardiogram (ECG), and multi-modal image data • Query across multiple types of primary and derived data located and maintained at different sites • Data integration • Federated querying

  3. The CardioVascular Research Grid (CVRG) Project Addresses These Needs • CVRG = a community resource for the distributed management, semantic description, federation, and analysis of bothprimary and derived CV data • CVRG is • developing new CV-specific data management resources • delivering CV data analysis tools and customized workflows • creating easy to use, customized interfaces for accessing its resources • providing these resources in a secure manner

  4. The CVRG Approach to Technology Development • Don’t re-invent the wheel • When possible, re-use or extend software components from other BioGrid projects (e.g., BIRN and the cancer Biomedical Informatics Grid (caBIG)) • Address the special needs of the CV community • Time-evolving imagery • ECG and EP data • CV-specific ontologies • Integration of multi-scale data sets • Specific data analysis workflows • Assure that the CV community drives technology development • Subject Matter Expert Ontology Review • CV Working Groups

  5. The CVRG Development Team Johns Hopkins University Resource PI: Raimond Winslow (rwinslow@jhu.edu) Project PI’s and Investigators: Donald Geman (Machine Learning) Michael Miller (Cardiac CA) Daniel Naiman (Machine Learning) Tilak Ratnanather (Cardiac CA) Raimond Winslow (ECG & EP Services) Laurent Younes (Cardiac CA) Project Manager: Stephen Granite (sgranite@jhu.edu) Developers: Timothy Brown (Cardiac CA Interfaces) Bill Girten (ECG Interfaces) David Hopkins (Website & Portal) Anthony Kolasny (Cardiac CA Interfaces) Luan Le (Imaging Interfaces) Kyle Reynolds (Systems Support) Michael Shipway (Machine Learning Interfaces) Matthew Toerper (Imaging Interfaces) Emory University Project PI’s and Investigators: Joel Saltz (caGrid) Tahsin Kurc (caGrid) Developers: Mark Grand (Lead Developer) Michel Monsour (Study Data Interfaces) Tony Pan (Imaging Interfaces) Ashish Sharma (Imaging Interfaces) caGrid Knowledge Center Justin Permar (caGrid) Steve Langella (Security) UCSD (BIRN; Yrs 1-2) Mark Ellisman Jeff Grethe Ramil Manansala

  6. What Data Services Does CVRG Provide? PACS (DCM4CHE), DICOM Data Service Proteomics Data Service ECG Data Service Virtual PACS CVRG Portal PortletAccess Federated Querying mRNA Expression Data Service Study Data Service (OpenClinica) Developed by CVRG Developed by NCI-caBIG Other Open Source SNP/GWAS Data Service Other CVRG Nodes

  7. Grid-enabling Existing Systems Federated Queries CVRG Node 3 (JHU) CVRG Node 2 (Emory) CVRG Node 1 (OSU) CVRG Portal Portlet Access Federated Querying ECG Data Service Data Service (“Lighthouse”) caGrid Introduce Study Data Service (OpenClinica) Abstract Model (Terminology, Semantics, Objects,…) NCI EVS, NCBO BioPortal Managed Locally Real Database (Oracle, DB2,…)

  8. What Analysis Services Does CVRG Provide? Heart Shape & Motion Analyses (LDDMM, PTA) CVRG Portal Workflow Access Machine Learning; Rweb, geWorkbench ECG Analysis Services Developed by CVRG Developed by NCI-caBIG Other Open Source Other CVRG Nodes

  9. ECG Data Management and Analysis:An Example of Novel Tool Development • Problem: • ECGs = most common type of biomedical time-series data • Primary data not always saved in digital format • Paper by default & multi-image DICOM in conjunction with ultrasound • Typically, only analytically derived data are stored • Difficult for clinical researchers to compare derived data (i.e., proprietary analysis methods) • Challenges: • No comprehensive data model/database implementation for managing digital ECG data existed at the start of the CVRG • Currently, no hardened ECG ontology is available for use in annotation • Few open-source web-based ECG analysis tools available to the CV community (e.g., Physionet; http://www.physionet.org/) • Solution: • Develop these resources

  10. ECG Analysis Workflows Reynolds Workflow ECG Analysis Parameters Norav ECG Data (RDT) Reynolds ECG Analysis Algorithms Results RDT -> HL7aECG ECG Data Data Storage Services Analytical Services Analysis Results Services Physionet Workflow WFDB ECG Data Physionet ECG Data (WFDB – header, data) Physionet ECG Analysis Algorithms Results Metadata XML

  11. Alpha CVRG Portal Demo • Alpha CVRG Portal leverages caGrid Portal and Web Single Sign-On (WebSSO) • caGrid Portal leverages LifeRay open-source Portal Server (http://www.liferay.com/) • caGrid WebSSO leverages JA-SIG Central Authentication Service (http://www.jasig.org/cas/) • Portlets implemented: • Beta Signup • ECG Storage/Analysis Workflow (end-to-end for both workflows) • Imaging Storage & Analysis (i.e., virtualPACS utilization and Large Diffeomorphic Deformation Metric Mapping (LDDMM)) • ProteinDB utilization • Beta Signup/ECG Workflow Demo

  12. Ontologies: • Emerging Strategy • When ontologies/tools are available from BIRN, caBIG and the NCBO (http://bioportal.bioontology.org/), use them • Develop new ontologies that are of special importance in CV research • CV EP Ontology • Version 1 complete • ECG Ontology • Version 1 nearing completion • CV Imaging Ontologies • Beginning stages, with the National Alliance for Medical Image Computing (NAMIC) • Deposit new CV ontologies at NCBO • These ontologies are then available for use by BIRN and caBIG

  13. Collaboration With the CVRG Project • CVRG Collaboration Proposals (PAR-07-426, as amended in NOT-08-103) • Development of a Hypertrophic Cardiomyopathy Consortium (Abraham) • Federation and Analysis of MESA Genetic and Imaging Data Using the CVRG (Lima) • Data Ontologies for Integrating Cardiovascular Epidemiological Studies (Rice) • Final LOI August 21 2009, final proposal submission September 21 2009 • One BRP collaboration proposal submitted • CardioVascular Imaging Informatics (CVRG & NAMIC Collaboration, PI = Kikinis) • NCBO Driving Biological Project • Ontology-Based Annotation of Biomedical Time-Series Data (Winslow) • Physionet (analytical services) • Site beta testers (request account on CVRG portal) • Working Groups, Conference on CardioVascular Informatics

  14. CVRG Web Resources • CVRG Website – Information about the CVRG (http://www.cvrgrid.org/) • CVRG Portal – Web environment for accessing all tools • Link on the CVRG Website • CVRG Wiki – Technical information on all services • Link on the CVRG Website • Software dependencies and revisions • Build information • Installation instructions

  15. Questions and Discussion

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