the cardiovascular research grid (cvrg): a national infrastructure for representing, sharing,...
Post on 26-Dec-2015
216 Views
Preview:
TRANSCRIPT
The CardioVascular Research Grid (CVRG):The CardioVascular Research Grid (CVRG):A National Infrastructure for A National Infrastructure for Representing, Sharing, Analyzing, Representing, Sharing, Analyzing, and Modeling Cardiovascular Dataand Modeling Cardiovascular Data
Stephen J. Granite, MSDirector of Database/Software Development The Johns Hopkins University Center for Cardiovascular Bioinformatics and Modeling
Why Is There A Need For The Why Is There A Need For The CVRG?CVRG?
The challenge of how best to represent CV dataEmerging data representation standards are seldom used
No standards for representing and no culture of sharing electrophysiological data
The challenge of sharing dataNational initiatives in CV genetics, genomics and proteomics are underway but there is no direct, easy way to discover data
Facilitate data discovery
The challenge of how best to develop and deploy “hardened” data analysis workflows
The challenge of discovering new knowledge from the CV data itself
Grids And E-ScienceGrids And E-Science
Grids1 Interconnected networks of computers and storage systems
Running common software
Enabling resource sharing and problem solving in multi-institutional environments
E-Science Computationally intensive science carried out on grids
Science with immense data sets that require grid computing
Two major “bio-grids” are active todayThe Biomedical Informatics Research Network (BIRN; http://www.nbirn.net/)The Cancer Biomedical Informatics Grid (caBIG; http://cabig.nci.nih.gov/)1I. Foster and C. Kesselman (2004). The Grid: Blueprint for a New Computing Infrastructure.
Elsevier.
The Biomedical Informatics Research Network The Biomedical Informatics Research Network (BIRN)(BIRN)Principal Investigator: Mark Ellisman UCSDPrincipal Investigator: Mark Ellisman UCSD
Grid infrastructure for sharing, analyzing and visualizing brain imaging data sets32 participating research sites, > 400 investigators4 “driving biological projects”BIRN is a “bottom up” effort (scientific applications drive technology)
UCLA ImageAcquisition
BWH ImageSegmentation
JHU Shape Analysis
End User Shape
Visualization
NCI intramural research effort, with selected external collaboratorsDevelop open-source software
Enables cancer researchers to become a caBIG node
Share data with the cancer research community
Develop controlled vocabularies for describing cancer phenotypes and multi-scale dataDevelop grid analytic services for analyzing cancer data sets
The Cancer Biomedical Informatics Grid (caBIG)The Cancer Biomedical Informatics Grid (caBIG)caGrid Lead Development Team: Joel Saltz OSUcaGrid Lead Development Team: Joel Saltz OSU
Center studies the cause and treatment of Sudden Cardiac Death (SCD) in the setting of heart failure (HF)
HF is the primary U.S. hospital discharge diagnosis
Incidence of ~ 400,000 per year, prevalence of ~ 4.5 million
Prevalence increasing as population ages
Leading cause of SCD (30-50% of deaths are sudden)
Medical expenditures ~ $20 billion per year
Manifestation of HF occurs at multiple biological levels
Genetic Predisposition via Single/Multi-Gene Mutations
Modified Gene/Protein Expression
Electrophysiological Remodeling and Altered Cellular Function
Heart Shape and Motion Changes
Reduced cardiac output, mechanical pump failure
The CVRG Driving Biological Project The CVRG Driving Biological Project (DBP)(DBP)The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. Marban)Marban)
The CVRG Driving Biological Project The CVRG Driving Biological Project (DBP)(DBP)The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. The D. W. Reynolds Cardiovascular Clinical Research Center (PI - E. Marban)Marban)
Large patient cohort (~ 1,200) at high risk for SCDAll have received ICD placement to prevent SCDCollecting multi-scale data for all these patientsPatients with ICD firings are defined as high risk for SCD; patients without as low riskWithin the 1st year, only 5% of the ICDs implanted have actually firedChallenge – discover multi-scale biomarkers that are predictive of which patients should receive ICDs
Genetic Variability(SNPs)
Gene ExpressionProfiling
Protein ExpressionProfiling
Multi-ModalImaging
ElectrophysiologicalData
Multi-Scale Data
The CVRG ProjectThe CVRG Project
R24 NHLBI Resource, start date 3/1/07
3 development teamsWinslow, Geman, Miller, Naiman, Ratnanather, Younes (JHU)
Saltz, Kurc (OSU)
Ellisman, Grethe (UCSD)
AimsDevelop tools for representing, managing and sharing multi-scale data
SNP, genomic and proteomic data (Project 1)Electrophysiological data (Projects 1 & 2)Heart Shape and Motion (Cardiac Computational Anatomy) data (Projects 1, 3 & 4)
Use multi-scale data to discover biomarkers that predict need for ICD placement (Project 5)
The CVRG-Core (Projects 1-5)
EP Data
(Project 2)
Gene
Expression
(Project 1)
Project 1: The CVRG Core Project 1: The CVRG Core InfrastructureInfrastructure
Develop and deploy CVRG-Core middlewareReuse components and assure interoperability with BIRN and caBIGOpen-source software stack that instantiates a CVRG node
BioINTEGRATE
(Project 1)
BioMANAGE
(Project 1)
BioPORTAL
(Project 1)
SNP
(Project 1)Imaging
(Project 1)
Patient/
Study
(Project 1)
Protein
Expression
(Project 1)
Multiple Analytical Methods
(Projects 2,3,4&5)
Data Services
CVRG Data Services CVRG Analytic Services
Project 2: Electrophysiological (EP) Project 2: Electrophysiological (EP) Data Management And DisseminationData Management And Dissemination
ECG EP data
ONTOLOGIES
XMLDATABASES
ECG & EPData AnalysisPortal
Goal Adopt/develop data models to represent cardiovascular EP dataCreate databases for managing and sharing these data
Project 3: Mathematical Project 3: Mathematical Characterization Of Cardiac Ventricular Characterization Of Cardiac Ventricular Anatomic Shape And MotionAnatomic Shape And Motion
Goal Develop methods for statistically characterizing variability of heart shape and motion in health and disease Use these methods to discover shape and motion biomarkers for CV disease
MethodsMeasure heart shape and motion over time in the Reynolds population using multi-modal imaging (MR, multi-detector CT and Gd+ contrast-enhanced MR)Model variation of heart shape/motion in both the low/high risk Reynolds patientsDiscover shape and motion parameters that predict who should receive ICD placement
Cardiac Computational Cardiac Computational Anatomy And Shape Analysis Anatomy And Shape Analysis LLarge arge DDeformation eformation DDiffeomorphic iffeomorphic MMetric etric MMappingapping22
2Beg et al (2004). Mag. Res. Med. 52: 1167 Template
(smoothed)
Targets(Diseased Training Set)
Targets(Normal Training Set) ?
Cardiac Computational Cardiac Computational Anatomy And Shape Analysis Anatomy And Shape Analysis LLarge arge DDeformation eformation DDiffeomorphic iffeomorphic MMetric etric MMappingapping22
2Beg et al (2004). Mag. Res. Med. 52: 1167 Template
(smoothed)
Targets(Diseased Training Set)
Targets(Normal Training Set)
DiseasedHeart
Project 4: Grid-Tools for Cardiac Computational Project 4: Grid-Tools for Cardiac Computational AnatomyAnatomy
Segmentatio
n
Landmarking, Affine &LDDMM Shape Analysis
BioMANAGE
Visualization
12
3
4 5
Supercomputing
TeraGrid
De-identificationand upload
6
Statistical
Analysis
Goal – predict risk of SCD and identify patients to receive ICDs
Develop learning methods that work in the “small sample regime”
Patient A Patient A
SCD HIGH RISK
Patient B Patient B
SCD LOW RISK
Deploy these multi-scale biomarker discovery tools on the CVRG Portal
Project 5: Statistical Learning Project 5: Statistical Learning With Multi-Scale Cardiovascular DataWith Multi-Scale Cardiovascular Data
Algorithms3-
6
3Geman et al (2004). Stat. Appl. Genet. Mol. Biol. 3(1): Article 19.4Xu et al (2005). Bioinformatics, 21(20): 3905-39115Anderson et al (2007). Proteomics 7(8): 11976Price et al (2007). PNAS 104(9): 3414
Project 6: Resource Project 6: Resource ManagementManagement
Establish CVRG Working Groups to create a mechanism for community input on design and function of CVRG-Core and the CVRG
CV ontologies/data models Testbed Projects
(HLB-STAT)
New Technologies Data Sharing/IRB
Undertake outreach efforts to inform, train, and support researchers in use of CVRG tools and resources
AcknowledgementsAcknowledgementsThe CVRG Development Team
Johns HopkinsUniversity
Siamak Ardekani
Donald Geman
Stephen Granite
Joe Henessy
David Hopkins
Anthony Kolasny
Aaron Lucas
Michael Miller
Daniel Naiman
Tilak Ratnanather
Kyle Reynolds
Aik Tan
Rai Winslow
Gem Yang
Ohio StateUniversity
Shannon Hastings
Tahsin Kurc
Stephen Langella
Scott Oster
Tony Pan
Justin Permar
Joel Saltz
UCSD
Mark Ellisman
Jeff Grethe
Ramil Manasala
NHLBI (R24 HL085343)
Microsoft Research Faculty Summit 2007
top related