1 / 15

Sponsoring Cancer Center Lombardi Comprehensive Cancer Center Georgetown University Workspaces Architecture (develop

Integrative Cancer Research. Sponsoring Cancer Center Lombardi Comprehensive Cancer Center Georgetown University Workspaces Architecture (developer) Integrative Cancer Research (developer) LCCC caBIG Representative Robert Clarke, Ph.D., D.Sc. clarker@georgetown.edu

bin
Download Presentation

Sponsoring Cancer Center Lombardi Comprehensive Cancer Center Georgetown University Workspaces Architecture (develop

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Integrative Cancer Research • Sponsoring Cancer Center • Lombardi Comprehensive Cancer Center • Georgetown University • Workspaces • Architecture (developer) • Integrative Cancer Research (developer) • LCCC caBIG Representative • Robert Clarke, Ph.D., D.Sc. • clarker@georgetown.edu • (202) 687-3755

  2. Integrated Cancer Research Teams • Georgetown University • Edmund Gehan (Biostatistics & Biomathematics) • Stephen Moore (Advanced Research Computing) • Seong Ki Mun (Imaging Science & Information Systems) • Cathy Wu (Protein Information Resource) • Virginia Tech • Joseph Wang (Engineering & Computer Science) • Catholic University • Jason Xuan (Engineering & Computer Science) • National Institutes of Health • Spiderweb Team (NCICB ) • Javed Khan (NHGRI) • Aiyi Liu (NICHD) • University of Edinburgh, Scotland • William Miller (Oncology)

  3. Overview • Project Activity • Tool development for exploring very high dimensional data sets • Deployment of grid enabled and integrated array (MIAME • compliant) and clinical research databases (Spiderweb) • Contribution of expression array and other data from multiple • clinical, translational, and basic research projects • Stage of Maturity • Tools at each stage of development • Technical Details/Standards • Open source; currently most are written in C++ and/or MatLab • Points of Interoperability • Use of caCORE APIs; caBIG-compatible APIs • Resources • Platform migration; software engineering; personnel

  4. Examples of Ongoing Funded Projects • Bioengineering Research Partnership (NCI) • Large, prospective, molecular profiling study in breast cancer • Expression array tool development and deployment • Expression array and clinical data • Gene/Nutrition Interactions in Breast Cancer (NCI) • U54 program project: array tool development and deployment; data • Breast Cancer Center of Excellence (DOD) • Study of alcohol and breast cancer risk; array tool deployment; data • Clinical Translational Research Study (DOD) • Large, prospective and retrospective, molecular profiling study • of Letrozole and Tamoxifen in breast cancer • Array tool development and deployment; data

  5. Examples of Ongoing Funded Projects • Computational Decomposition of Composite Molecular • Signatures (NCBIB) • Expression array tool development, optimization, and deployment • Intelligent Mapping and Visual Exploration of Gene • Expression Profiles (NCI) • Expression array tool development, optimization, and deployment • Comprehensive Computational Analysis of Gene Expression • (NCI) • Expression array tool development, optimization, and deployment

  6. Population-based gene expression profiling & statistical data analysis Imaging (dynamic & longitudinal) of biological events and functions Time-course based discovery of gene regulatory networks Integrated Tool Development

  7. Tools for Very High Dimensional Data Sets • General Approach • Probablistic approaches to address the properties of very high • dimensional data spaces (e.g., "curse of dimensionality", • "concentration of measure phenomenon") • Data Preprocessing • Normalization • Tissue heterogeneity correction • Multitask, goal-specific, gene selection tool (e.g., classification vs. • signaling/function) • Cluster Discovery and Visualization • Visual Statistical Data Analysis (VISDA) package • Classification and Prediction • Optimized multilayer perceptron classifiers • Expression array tool development

  8. C++ and R Packages dChip Software Bioconductor 3-D Visualization Toolkit OpenInventor Comprehensive Data Analysis Classification & Prediction Data Preprocessing Cluster Discovery & Visualization Adaptive Hierarchical Subspace Experts Cross-Phenotype Normalization Tissue Heterogeneity Correction Information Visualization Optimized Mutlilayer Perceptron Classifiers Multitask Gene Selection Visual Statistical Data Analyzer (VISDA) Matlab Neural Networks Pattern Recognition Independent Component Analysis

  9. Block Principal Components Analysis Tissue Heterogeneity Correction Examples of Novel Tools Discriminant Components Analysis

  10. Fast-flow Tumor ROI Slow-flow DCE-MRI of breast cancer 08/27/02 11/19/02 02/18/03 Dynamic Contrast Enhanced-MRI Tumor angiogenesis in the breast

  11. Improved Diagnosis and Therapeutic Assessment Image-Guided Intervention & Biopsy Optimization Drug Discovery & Assessment Functional/Molecular Imaging of Composite Signatures in Breast Cancer Molecular Analysis of Breast Cancer Image Analysis and Modeling Model-Based Image Segmentation Blind Source Separation Independent Component Analysis Deformable Image Registration 3-D Modeling of Tissue Components in Breast Integrating Imaging and Molecular Analysis

  12. Integrate Data:Protein Information Resource

  13. SpiderWeb Collaboration Goals • Integration of multiple, varied information to achieve a basis for rational design of novel diagnostics and therapeutics • Integration of functional genomics information into clinical information so it can be used to diagnose genetic predisposition, sub-classification of disease and help with optimal selection of therapies • Bring new efficiencies to clinical research by integrating bench to bedside and back

  14. Basic Science Investigation Clinical Research Drug Discovery Target Validation Target Delivery Drug Development SpiderWeb Clinical Trials Molecular Analysis Molecular Pathology Functional Genomics MolecularProfiles and Targets, Agents, Reagents, Proteins. Antibodies Study Definitions, Labs, Regimens, Investigators, Sites, Participants, Adverse Reactions, Outcomes Gene Expression Profiles, ESTs, Pathways, Protein Complexes, CGH and SAGE Data Cancer Models, Images, Tissues, Tumor Classifications, Molecular Signatures SpiderWeb SpiderWeb Project (LCCC/NCICB) Approval

  15. Timelines and Resources • Development Requirements (approx) • 3 programmers for data analysis tools • 3 programmers for PIR integration into caBIG • Infrastructure • C++/Java development environment enhancements • software system administrator • Draft 12-month Work Plan and Milestones (approx) • 1-3 months VISDA caBIG interoperability • 4-6 months CPN and THC caBIG interoperability • 7-9 months MTGS and MLP/AHSE caBIG interoperability • 10-12 months software system integration and final testing • 12 months for PIR integration into caBIG

More Related