CaGrid, Fog and Clouds Joel Saltz MD, PhD Director Center for Comprehensive Informatics.

Slides:



Advertisements
Similar presentations
CVRG Presenter Disclosure Information Joel Saltz MD, PhD Director Comprehensive Informatics Center Emory University Translational Research Informatics.
Advertisements

CVRG Presenter Disclosure Information Tahsin Kurc, PhD Center for Comprehensive Informatics Emory University CardioVascular Research Grid Core Infrastructure.
Pathology and Imaging In Biomarker Development C. Carl Jaffe, MD, FACC Cancer Imaging Program National Cancer Institute.
CHOICE Pathology Informatics 2010 Boston, Massachusetts DataReady ® : A Deployable Data Management and Integration System for Large-scale Cancer Repositories.
Who am I Gianluca Correndo PhD student (end of PhD) Work in the group of medical informatics (Paolo Terenziani) PhD thesis on contextualization techniques.
1 Introduction to XML. XML eXtensible implies that users define tag content Markup implies it is a coded document Language implies it is a metalanguage.
Overview of Biomedical Informatics Rakesh Nagarajan.
Introduction Integrative Analysis of Genomic Variants in Carcinogenesis Syed Haider, Arek Kasprzyk, Pietro Lio Artificial Intelligence and Computational.
CaGrid Service Metadata Scott Oster - Ohio State
RSNA 2010 Talking Points NCI had two booths: The caBIG exhibit which comprised of seven workstations showcasing the NBIA, AIM, Virtual PACS, AVT, XIP,
Image Query (IQ) Project Update Building queries one question mark at a time March, 2009.
Web-based Portal for Discovery, Retrieval and Visualization of Earth Science Datasets in Grid Environment Zhenping (Jane) Liu.
The cancer Biomedical Informatics Grid™ (caBIG™): In Vivo Imaging Workspace Projects Fred Prior, Ph.D. Mallinckrodt Institute of Radiology Washington University.
Clinical Trials, TCGA: Deep Integrative Research RT, Imaging, Pathology, “omics” Joel Saltz MD, PhD Director Center for Comprehensive Informatics.
Integrative Analysis of Pathology, Radiology and High Throughput Molecular Data Joel Saltz MD, PhD Director Center for Comprehensive Informatics.
Department of Biomedical Informatics Development of Ontology-anchored Grid-based Data Services to Facilitate Integrative Clinical and Translational Science.
Tony Pan, Ashish Sharma, Metin Gurcan Kun Huang, Gustavo Leone, Joel Saltz The Ohio State University Medical Center, Columbus OH gridIMAGE Microscopy:
Workflow Metadata John Koisch, Guidewire Architecture.
Imaging Workspace An Overview and Roadmap Eliot L. Siegel, MD Imaging Workspace Lead SME January 23, 2008.
Radiogenomics in glioblastoma multiforme
Department of Biomedical Informatics Service Oriented Bioscience Cluster at OSC Umit V. Catalyurek Associate Professor Dept. of Biomedical Informatics.
Introduction to MDA (Model Driven Architecture) CYT.
Introduction to Apache OODT Yang Li Mar 9, What is OODT Object Oriented Data Technology Science data management Archiving Systems that span scientific.
Department of Radiology and Imaging Sciences Division of Neuroradiology University School of Medicine.
The National Biomedical Imaging Archive (NBIA) In Action: An Introduction for Users A Tool Demonstration from caBIG® Presented by: Eliot Siegel, MD Maryland.
PLoS ONE Application Journal Publishing System (JPS) First application built on Topaz application framework Web 2.0 –Uses a template engine to display.
Data Management BIRN supports data intensive activities including: – Imaging, Microscopy, Genomics, Time Series, Analytics and more… BIRN utilities scale:
H Using the Open Metadata Registry (OpenMDR) to generate semantically annotated grid services Rakesh Dhaval, MS, Calixto Melean,
Prediction of Glioblastoma Multiforme Patient Time to Recurrence Using MR Image Features and Gene Expression Nicolasjilwan, M.1·Clifford, R.2·Flanders,
1 Senn, Information Technology, 3 rd Edition © 2004 Pearson Prentice Hall James A. Senn’s Information Technology, 3 rd Edition Chapter 12 Creating Web-Enabled.
© NlH National Center for Image Guided Therapy, 2012 ASNR 2012 Imaging Genomic mapping of Edema/Cellular Invasion MRI-Phenotypes in Glioblastoma Multiforme.
Nadir Saghar, Tony Pan, Ashish Sharma REST for Data Services.
ASNR 2012 Methodology for Imaging Genomics of Gliomas
Ashish Sharma, Tony Pan, Barla Cambazoglu, Joel Saltz Ohio State University, Columbus, OH (ashish, tpan, October 10, 2007 caBIG In Vivo.
©Ferenc Vajda 1 Semantic Grid Ferenc Vajda Computer and Automation Research Institute Hungarian Academy of Sciences.
1 Introduction and Welcome Joel Saltz MD, PhD Co-Director caGrid Knowledge Center caGrid Knowledge Center Workshop February 2011.
Copyright © 2006 YRP Ubiquitous Networking Laboratory. All rights reserved. 1 Lower case ubiquitous web architecture and its implementations Satoru TAKAGI.
CaGrid Overview and Core Services caGrid Knowledge Center February 2011.
GEON2 and OpenEarth Framework (OEF) Bradley Wallet School of Geology and Geophysics, University of Oklahoma
ACGT: Open Grid Services for Improving Medical Knowledge Discovery Stelios G. Sfakianakis, FORTH.
Semantic Enhancement: Key to Massive and Heterogeneous Data Pools Violeta Damjanovic, Thomas Kurz, Rupert Westenthaler, Wernher Behrendt, Andreas Gruber,
Introduction to caIntegrator caBIG ® Molecular Analysis Tools Knowledge Center April 3, 2011.
XML-Based Grid Data System for Bioinformatics Development Noppadon Khiripet, Ph.D Wasinee Rungsarityotin, MS Chularat Tanprasert, Ph.D Royol Chitradon.
16/11/ Semantic Web Services Language Requirements Presenter: Emilia Cimpian
NeuroLOG ANR-06-TLOG-024 Software technologies for integration of process and data in medical imaging A transitional.
NA-MIC National Alliance for Medical Image Computing Core 1b – Engineering Computational Platform Jim Miller GE Research.
In Vivo Imaging Middleware and Applications RSNA 2007 Berkant Barla Cambazoglu The Ohio State University Department of Biomedical Informatics.
Jin MENG Shen FU (DPD 08) Biology 2 - Head/Neck and CNS Tumors
CBioPortal Web resource for exploring, visualizing, and analyzing multidimentional cancer genomics data.
Towards Unifying Vector and Raster Data Models for Hybrid Spatial Regions Philip Dougherty.
1 ECCF Training Computationally Independent Model (CIM) ECCF Training Working Group January 2011.
A Portrait of the Semantic Web in Action Jeff Heflin and James Hendler IEEE Intelligent Systems December 6, 2010 Hyewon Lim.
The National Cancer Imaging Archive (NCIA) In Action: An Introduction for Users A Tool Demonstration from caBIG™ Carl Jaffe, MD NCI-Cancer Imaging Program.
High throughput biology data management and data intensive computing drivers George Michaels.
Imaging Workspace An Overview and Roadmap Eliot L. Siegel, MD Imaging Workspace Lead SME January 23, 2008.
Tony Pan, Stephen Langella, Shannon Hastings, Scott Oster, Ashish Sharma, Metin Gurcan, Tahsin Kurc, Joel Saltz Department of Biomedical Informatics The.
Case Study: HL7 Conformance in VA Imaging Mike Henderson Principal Consultant Eastern Informatics, Inc.
0 caBIG and caGrid: Interoperable Computing Infrastructure for the Nation’s [and World’s] Cancer Research Enterprise Peter A. Covitz, Ph.D. Chief Operating.
The Contribution of caBIG and In Silico Resources to Glioblastoma Research Daniel J. Brat MD, PhD Department of Pathology and Laboratory Medicine Emory.
Genomic Medicine Grid Juan Pedro Sánchez Merino Instituto de Salud Carlos III
CTTI PROJECT Emory University, Quality Assurance and Review Center (QARC) and Washington University in St. Louis.
PD: Joel Saltz, MD, PhD PI: Daniel J. Brat MD, PhD Emory University School of Medicine In Silico Center for Brain Tumor Research.
A web portal for management of biological data and applications
Tools and Services Workshop
Joslynn Lee – Data Science Educator
Pathology Spatial Analysis February 2017
Fred Prior, Ph.D. Mallinckrodt Institute of Radiology
Joel Saltz MD, PhD Director Center for Comprehensive Informatics
Presenters: Joel Saltz, Biomedical Informatics, Stony Brook University
Database Systems Instructor Name: Lecture-3.
Presentation transcript:

caGrid, Fog and Clouds Joel Saltz MD, PhD Director Center for Comprehensive Informatics

Overview Introduction Tools Use Case Fog and Clouds

Fog Computing Grid interoperable enterprise virtualization

caGrid, Enterprise and Clouds

Biomedical Middleware: caGrid, TRIAD, i2b2 caGrid Components –Security (GAARDS) –Language (metadata, ontologies) –Semantic/Federated query –Workflow –Grid Service Graphical Development Toolkit (Introduce) –DICOM, IHE compatibility –Advertisement and Discovery

Preferred Name Synonyms Definition Relationships Concept Code Vocabulary/Ontology

Interoperability –Registered metadata –Ontology concept codes used to annotate models –XML schemas that define data structures also registered –Thus both data semantics AND data structures are registered. That is how we achieve (relative) interoperability.

Use Case: Will Treatment work and if not, why not? Avastin and Glioblastoma in RTOG-0825 Treatment: Radiation therapy and Avastin (anti angiogenesis) Predict and Explain: Genetic, gene expression, microRNA, Pathology, Imaging RT, imaging, Pathology markup/annotations/query Active Data workflows

Avastin and GBMs in RTOG-0825 Analysis on pre-treatment tissue to extract imaging and molecular biomarkers that are indicative of Outcome/Avastin response. whole genome mRNA and microRNA expression profiling of GBM tumor specimens to identify outcome/Avastin response biomarkers Analyzing the Pathology imaging and diagnostic imaging registered with the therapy plan to extract any biomarkers that can indicate Avastin response. Does advanced imaging (eg: diffusion weighted imaging) provide markers that can predict patient response? RT, Diagnostic Imaging and Pathology: Support Human/Algorithm analyses, annotation, markup Data management and display framework that integrates the pathology with the radiology, therapy treatment information and the clinical data. This involves integrating platforms that manage imaging data at ACRIN, pathology at UCSF and molecular data from Emory.

For the sake of quality control, reproducibility and data sharing, results of RT, imaging, Pathology observations and analyses need to be described in a well defined manner Finding: mass Mass ID: 1 Margins: spiculated Length: 2.3cm Width: 1.2cm Cavitary: Y Calcified: N Spatial relationships: Abuts pleural surface; invades aorta

Distinguish (and maybe redefine) astrocytic, oligodendroglial and oligoastrocytic tumors using TCGA and Rembrandt Important since treatment and Outcome differ Link nuclear shape, texture to biological and clinical behavior How is nuclear shape, texture related to gene expression category defined by clustering analysis of Rembrandt data sets? Relate nuclear morphometry and gene expression to neuroimaging features (Vasari feature set) Genetic and gene expression correlates of high resolution nuclear morphometry and relation to MR features using Rembrandt and TCGA datasets.

Annotation and Markup of Pathology Data needs Human/Algorithm Cooperation Astrocytoma vs Oligodendroglima TCGA finds genetic, gene expression overlap Pathologists have also long seen overlap Relationship between Pathology, Molecular, Radiology Relationship to Outcome, treatment response

Example: Compute Intensive Workflow

Fog Computing

Attributes of Fog Computing For legal and organizational reasons, data location is constrained Virtual machines used to create software stacks that use caGrid + other middleware to expose data services (we regularly ship VMs with caGrid based software stacks) Enterprises such as Emory increasingly rely on virtualization architectures Mid-range active storage platforms (eg Emory 1PB archival, 100TB fast disk, 2K cores, infiniband interconnect) will rely heavily on virtualization

Relationship between Fog and Cloud Computing Workflows have enterprise, caGrid and compute intensive components Interoperability between enterprise and caGrid software stacks major current NCI/ONR issue Given virtualization, HPC and large scale data requirements can be tackled with cloud approach

Issues Organizational, legal requirements create constraints on placement of datasets, where VMs can be run Mapping of VMs, datasets: –Huge variation in communication, I/O bandwidth, compute capabilities in Fog/Cloud continua –Multiple software stacks, heterogeneous hardware architectures Security: Fog to Cloud: multiple distinct but overlapping security related requirements and constraints

Thank you