Biomarker Strategies

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Biomarker Strategy talk presented at the PRISM forum Special Interest Group: "Information Challenges in the Age of Biomarkers”, October 2009

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  • Biology PhDs? MBAs?
  • Instrument Variability must be far less than biological variability to see anything! Power studies determine the number of subjects required to see biological effects
  • Power is probability of correctly declaring association between analyte intensity and disease (OR=2)
  • The real, biological relationship between analytes is reflected by correlations All pathway analysis tools rely on MFC, assumes Gaussian distribution
  • 336,980,250,000
  • Biomarker Strategies

    1. 1. It’s the Narrative… Informatics Strategies to Maximize Biomarker Impact Part of the Special Interest Group: Information Challenges in the Age of Biomarkers Thomas Plasterer, PhD October 13, 2009
    2. 2. Overview <ul><li>Introduction to BG Medicine </li></ul><ul><li>Biology and Medicine drives the business </li></ul><ul><li>Measuring systems and Biomarkers </li></ul><ul><li>It’s the narrative </li></ul>
    3. 3. History of BG Medicine <ul><li>Founded in 2000 (As Beyond Genomics) </li></ul><ul><li>Systems approach to biomarker discovery for: </li></ul><ul><ul><ul><li>Biomarker-guided medicine (novel diagnostics) </li></ul></ul></ul><ul><ul><ul><li>Pharmaceutical R&D </li></ul></ul></ul><ul><li>Production Scale Technology Platforms: </li></ul><ul><ul><li>Capacity for 16-20 discovery projects/year </li></ul></ul><ul><ul><li>Automated computational platform, semi-automated bioinformatics, and advanced data visualization </li></ul></ul><ul><ul><li>Samples to analysis in 120 days </li></ul></ul><ul><li>Experience: </li></ul><ul><ul><li>CNS, CVD, Metabolic Syndrome, Oncology, Toxicology, Infectious Disease </li></ul></ul><ul><ul><li>Plasma, Serum, Urine, CSF, Liver, Adipose, Muscle, Plaque </li></ul></ul>
    4. 4. Proteomics Metabolomics Genomics Blood, Urine, Tissues Clinical, Preclinical High Resolution Measurements BG Medicine Technology Platform IP-Protected Technology Suite to Maximize Novel Biomarker Discovery Data Analysis / Integration / Interpretation / Visualization
    5. 5. Putting the Pieces Together for Discovery, Validation and Diagnostics Define an Opportunity Discovery and Validation Pharma Partnerships Payor Partnership Products Design Study and Obtain Samples Market Potential Copenhagen Heart Study Framingham Heart Study <ul><li>Early disease detection and prevention </li></ul><ul><li>Patient stratification </li></ul><ul><li>Drug safety and toxicity </li></ul><ul><li>Disease monitoring and prognosis </li></ul>Novel blood-based diagnostics
    6. 6. BG Medicine: Enabling Biomarker-Guided Medicine We discover and develop novel diagnostics in cardiovascular disease and cancer Address unmet need for diagnostics that match patients and treatments to provide more effective, less costly and safer therapies Exceptional portfolio of cardiovascular product candidates to address important unmet medical needs Flexible and highly scalable discovery platform enables us to align with multiple constituencies
    7. 7. Biology Drives the Business <ul><li>Stay close to the science and medicine—It’s the lifeblood of the Company </li></ul><ul><li>Lou Lavigne </li></ul><ul><li>(EVP/CFO Genentech 1997-2005) </li></ul><ul><li>Babson Business of BioPharma Course </li></ul><ul><li>November, 2008 </li></ul>
    8. 8. Molecular Systems Analysis – BGM Workflows Fully Integrated Operations, QA/QC, IT Data Mgmt, LIMS & Oracle Pipeline Clear, measurable objective Protein Analysis Transcript Analysis Statistical Modeling Qualified Biomarker Additional Analyses Master Dataset Metabolite Analysis Correlation Analysis Biomarkers -Predictive -Prognostic -Diagnostic Population-based Biochemical Similarity Biomarker Context Biological Data Mining Exploratory Analysis Sufficiently powered study design
    9. 9. BGM’s Systems Biology Information System (SBIS) LIMS Metabolomics database Proteomics database Statistics database CORRNET BTE SEER Omics database Bioanalytical Data Generation Statistical Analysis Bioinformatics Transcriptomics database Corporate database Pipeline database Report database workflow and processes security and management reporting and data mining LIMS API AIMS API MXSUITE API PXSUITE API STATDB API CORPDB API Oracle PL/SQL API (packages, procedures) Java Domain Objects / Data Access Objects SEER API Resource Tier Oracle 10G on NAS Integration Tier Oracle PL/SQL Business Tier Java, PL/SQL, Open source framework Presentation Tier JSP / Servlet, Java, RMI, DHTML Open source frameworks Client Tier HTML BGM SYSTEM BIOLOGY INFORMATION SYSTEM (SBIS) LIMS AIMS MXSUITE PXSUITE STATS SEER REPORT BGM Central Authentication Service (BGM CAS) Pipeline Client server
    10. 10. Experimental Design Sidney Harris, New Yorker
    11. 11. Biomarker Discovery—Metabolic Syndrome Example <ul><li>Measuring in multiple platforms (profiling & targeted) offers the greatest pool of potentially significant analytes </li></ul><ul><li>Analytes are up or down – where? </li></ul><ul><li>Can circulating biomarkers be linked directly to mechanism? </li></ul><ul><li>Characterization of Biomarkers in context informs go/no go decisions </li></ul>Cheng AY and Fantus G; CMAJ 2005; 172(2): 213-26
    12. 12. Fully Integrated Operations, QA/QC, IT Data Mgmt, LIMS & Oracle Pipeline Clear, measurable objective Protein Analysis Transcript Analysis Statistical Modeling Qualified Biomarker Additional Analyses Master Dataset Metabolite Analysis Correlation Analysis Biomarkers -Predictive -Prognostic -Diagnostic Population-based Biochemical Similarity Biomarker Context Biological Data Mining Exploratory Analysis Molecular Systems Analysis – BGM Workflows Sufficiently powered study design
    13. 13. Achieving Objectives with Project Work Plans <ul><li>Align study goals & objectives, samples, platforms, biostatistics and bioinformatics </li></ul><ul><li>Determine basics of statistics and bioinformatic analyses </li></ul><ul><ul><li>Perform power calculations </li></ul></ul><ul><ul><li>Determine if development required </li></ul></ul><ul><li>Determine Key Decision Points </li></ul><ul><li>Establish study limitations and potential next steps </li></ul>Assembling the Study
    14. 14. Resent CVD Study Example
    15. 15. Upfront Preparations Determine Success <ul><li>Variances of most analytes (metabolomic and proteomic platforms) are found to be 0.1 and 1, with animal studies lying on the lower end of the spectrum </li></ul><ul><li>Mean fold changes (MFCs) of significant analytes in human and animal studies are found to range between 1.5 – 2.0 and 10-20, respectively </li></ul>Based on Experience @ BGM over several years: Animal Studies Human Studies
    16. 16. Power Calculations <ul><li>The power in biomarker discovery is a function of: </li></ul><ul><ul><li>The sample size </li></ul></ul><ul><ul><li>The separation between the groups </li></ul></ul><ul><ul><li>The proportion of biomarkers </li></ul></ul><ul><ul><li>The false discovery rate (FDR) allowed </li></ul></ul><ul><ul><li>The number of samples for pooling </li></ul></ul><ul><ul><li>The platform variability </li></ul></ul><ul><ul><li>The within-group variability </li></ul></ul><ul><ul><li>Other factors (e.g. other covariates) </li></ul></ul>? <ul><li>Statistical power = probability to detect biomarkers </li></ul>
    17. 17. Power Calculation for Multivariate Biomarkers (e.g. Regression) <ul><li>Classical Setting </li></ul><ul><li>n > p </li></ul><ul><li>Linear regression model </li></ul><ul><li>Parametric (F) test of model significance </li></ul><ul><li>Computationally inexpensive </li></ul><ul><li>Biomarker Discovery Setting </li></ul><ul><li>n << p </li></ul><ul><li>Regression with constraints on parameters (elastic net) </li></ul><ul><li>Dimensionality reduction needed (through cross-validation) </li></ul><ul><li>Non-parametric (label permutations) test of model significance </li></ul><ul><li>Computationally very expensive </li></ul>
    18. 18. Aligning Platforms and Experiments <ul><li>Metabolomics </li></ul><ul><ul><li>LC-MS profiling for lipids, small polar molecules, amino acids </li></ul></ul><ul><ul><li>GC-MS profiling for organic acids, sugars, fatty acids, amino acids, alcohols, nucleobases </li></ul></ul><ul><ul><li>Targeted analysis by multiple reaction monitoring (MRM) </li></ul></ul><ul><ul><li>Use of qualified vendors for additional targeted platforms </li></ul></ul><ul><li>Proteomics </li></ul><ul><ul><li>Discovery proteomics (iTRAQ MALDI) </li></ul></ul><ul><ul><li>Targeted proteomics with multiplexed immunoassays and MRM </li></ul></ul><ul><li>Transcriptomics </li></ul><ul><ul><li>Partnering strategy for gene, exon microarrays, copy number, etc. </li></ul></ul><ul><li>Clinical Chemistry </li></ul><ul><li>Imaging </li></ul>
    19. 19. The impact of New Measurement Modalities <ul><li>ChIP-chip </li></ul><ul><li>Micro RNA </li></ul><ul><li>Exon Microarrays </li></ul><ul><li>Whole Genome/SNP Genotyping </li></ul><ul><li>Whole Genome/Copy Number Variants </li></ul><ul><li>Personal Genomes and Next Generation Sequencing </li></ul><ul><li>Can these technologies handle throughput needed to appropriately power an experiment? </li></ul><ul><li>Storage considerations </li></ul>
    20. 20. Molecular Systems Analysis – BGM Workflows Fully Integrated Operations, QA/QC, IT Data Mgmt, LIMS & Oracle Pipeline Clear, measurable objective Protein Analysis Transcript Analysis Statistical Modeling Qualified Biomarker Sufficiently powered study design Additional Analyses Master Dataset Metabolite Analysis Correlation Analysis Biomarkers -Predictive -Prognostic -Diagnostic Population-based Biochemical Similarity Biomarker Context Biological Data Mining Molecular Systems Analysis – BGM Workflows Exploratory Analysis
    21. 21. Biostatics, Biointegration and Bioinformatics <ul><li>Study Design </li></ul><ul><li>Platform Processes </li></ul><ul><ul><li>Normalization </li></ul></ul><ul><ul><li>Batch Correction </li></ul></ul><ul><ul><li>Filtering </li></ul></ul><ul><li>Statistical Analysis </li></ul><ul><ul><li>Exploratory Analysis </li></ul></ul><ul><ul><li>Statistical Modeling </li></ul></ul><ul><li>BioContextualization </li></ul><ul><ul><li>Pathway Analysis </li></ul></ul><ul><ul><li>Correlation Analysis </li></ul></ul>
    22. 22. Let the Data Speak <ul><li>Unsupervised Discovery… </li></ul><ul><ul><li>Statistical Modeling </li></ul></ul><ul><ul><ul><li>Univariate and multivariate markers </li></ul></ul></ul><ul><ul><ul><li>Classifiers </li></ul></ul></ul><ul><ul><li>Cluster analysis </li></ul></ul><ul><ul><ul><li>PCA, PCDA </li></ul></ul></ul><ul><ul><ul><li>COSA </li></ul></ul></ul><ul><ul><li>Correlation Analysis </li></ul></ul><ul><li>… followed by Supervised Interpretation </li></ul><ul><ul><li>Map networks to: </li></ul></ul><ul><ul><ul><li>Ontologies </li></ul></ul></ul><ul><ul><ul><li>Pathways, Reactions </li></ul></ul></ul><ul><ul><ul><li>Literature </li></ul></ul></ul><ul><li>Unknowns? Validation? </li></ul>
    23. 23. <ul><li>Diversity of Types of Outcome Measures </li></ul><ul><ul><li>Dichotomous (e.g. yes/no PFS > X years) </li></ul></ul><ul><ul><li>Ordinal (e.g. Disease Severity) </li></ul></ul><ul><ul><li>Continuous (e.g. glucose level) </li></ul></ul><ul><ul><li>Categorical (e.g. Disease1 vs. Disease2) </li></ul></ul><ul><li>Possible Biomarker Objectives </li></ul><ul><ul><li>Diagnostic </li></ul></ul><ul><ul><li>Predictive </li></ul></ul><ul><ul><li>Prognostic </li></ul></ul><ul><li>N analytes » N samples (in Discovery Phase) </li></ul>Statistical Modeling
    24. 24. <ul><li>Select final, minimal set of molecules which will comprise biomarker </li></ul><ul><ul><li>Reduce number of molecules needed </li></ul></ul><ul><ul><li>Select only the most informative analytes for final assay development and deployment </li></ul></ul><ul><li>Construct parametric statistical models to identify statistically significant differences among groups </li></ul><ul><li>Determine performance metrics of marker test </li></ul><ul><ul><li>Sensitivity, specificity, PPV, NPV </li></ul></ul>Statistical Modeling for Biomarkers
    25. 25. Recursive Feature Reduction in Classifiers reduce number of analytes at each step
    26. 26. Automation of Biostatistics <ul><li>Automated Multivariate Classifiers (AMC) </li></ul><ul><ul><li>Classification algorithms: </li></ul></ul><ul><ul><ul><li>Random Forest </li></ul></ul></ul><ul><ul><ul><li>PAM </li></ul></ul></ul><ul><ul><ul><li>Elastic Net </li></ul></ul></ul><ul><ul><li>Web based application: </li></ul></ul><ul><ul><ul><li>Automated sample classification </li></ul></ul></ul><ul><ul><ul><li>Automated marker selection </li></ul></ul></ul><ul><ul><ul><li>Automated generation of custom analysis report </li></ul></ul></ul><ul><ul><ul><li>Dynamic identification update </li></ul></ul></ul><ul><li>Parallel Computing Infrastructure (PCI) </li></ul><ul><ul><li>Software solution for high performance computing to leverage the Cluster </li></ul></ul>
    27. 27. Molecular Systems Analysis – BGM Workflows Fully Integrated Operations, QA/QC, IT Data Mgmt, LIMS & Oracle Pipeline Clear, measurable objective Protein Analysis Transcript Analysis Statistical Modeling Qualified Biomarker Sufficiently powered study design Additional Analyses Master Dataset Metabolite Analysis Correlation Analysis Biomarkers -Predictive -Prognostic -Diagnostic Population-based Biochemical Similarity Biomarker Context Biological Data Mining Molecular Systems Analysis – BGM Workflows Exploratory Analysis
    28. 28. Data Visualization/Exploratory Statistics <ul><li>Principal Component Analysis (PCA) </li></ul><ul><li>Hierarchical clustering </li></ul><ul><li>Heat Maps </li></ul><ul><li>Used to get a global view of the data, locate potential outliers </li></ul><ul><li>Use to find trends that may fall below p-value, false discovery (q-value) thresholds </li></ul><ul><li>Helps to set the landscape for biomarker contextualization and transitioning from stats to biology (hint: key part of the narrative) </li></ul>
    29. 29. Molecular Systems Analysis – BGM Workflows Fully Integrated Operations, QA/QC, IT Data Mgmt, LIMS & Oracle Pipeline Clear, measurable objective Protein Analysis Transcript Analysis Statistical Modeling Qualified Biomarker Sufficiently powered study design Additional Analyses Master Dataset Metabolite Analysis Correlation Analysis Biomarkers -Predictive -Prognostic -Diagnostic Population-based Biochemical Similarity Biomarker Context Biological Data Mining Molecular Systems Analysis – BGM Workflows Exploratory Analysis
    30. 30. In Systems, Analytes Interact <ul><li>Weaknesses of the List Paradigm: </li></ul><ul><ul><li>Univariate statistics order analysis focus </li></ul></ul><ul><ul><li>Are the most differentially expressed analytes the ones to focus upon? </li></ul></ul><ul><li>Associations and Correlations </li></ul><ul><ul><li>Observed biological variability often exceeds the relative technical standard deviation </li></ul></ul><ul><ul><li>Biological variation is frequently not independent and is usually associated with the variation of other analytes </li></ul></ul>
    31. 31. Biocontextualization Rationale <ul><li>Do we need to know the role of our significant peaks? </li></ul><ul><ul><li>E.G.: SELDI biomarkers in breast cancer </li></ul></ul><ul><li>FDA ‘validated biomarker’ concept requires some knowledge of the biology </li></ul><ul><li>Multiple projects (even those without a planned contextualization step) required biomarkers in some relevant biological context </li></ul>
    32. 32. Biocontextualization Approaches <ul><li>For Mechanism Projects: </li></ul><ul><ul><li>Place univariate markers into biological context using knowledgebases: </li></ul></ul><ul><ul><ul><li>BTE, IPKB (Ingenuity), PKB (Proteome) </li></ul></ul></ul><ul><ul><li>Evaluate relationships (i.e. correlations) using CNA™ </li></ul></ul><ul><ul><li>Network size & analyte count are challenges </li></ul></ul><ul><li>For Biomarker Projects: </li></ul><ul><ul><li>Attempt to contextualize minimal biomarker sets using similar approaches </li></ul></ul><ul><ul><li>Statistical goal of a reduced biomarker set not in accord with contextualization approach </li></ul></ul>Must have tight alignment between statistics and bioinformatics and biocontextualization
    33. 33. Why a Pathway Mapping Strategy is Dangerous <ul><li>Frequently out of context </li></ul><ul><ul><li>By location (tissue, cell type) </li></ul></ul><ul><ul><li>By species </li></ul></ul><ul><li>Background probabilities assumptions may not be appropriate </li></ul><ul><li>Analyte Attrition </li></ul>
    34. 34. <ul><li>Correlations are an unsupervised method to discover novel relationships between measured analytes </li></ul><ul><li>Takes advantage of individual variation in analyte levels amongst the members of an experimental group (state) </li></ul><ul><li>Correlation value (r) is calculated between each pair of analytes using the values for each individual in a group </li></ul>Analyte Correlations r = 0.96 Example of Positive Correlation 2 4 6 8 10 15 20 25 Animal Number 1 2 3 4 5 6 7 8 9 10 12.5 15 17.5 20 22.5 25 5.2 5.28 5.37 5.45 5.54 5.62 5.7 Serum HDL mRNA 1418862_at 10 12 14 16 18 20 22 24 5.2 5.3 5.4 5.5 5.6 Serum HDL 1418862_at
    35. 35. Analyte Correlations Example of Negative Correlation r = -0.93 2 4 6 8 30 40 50 60 70 Animal Number 1 2 3 4 5 6 7 8 9 9.42 9.58 9.73 9.89 10.04 Serum HDL Lipid LCMS 554-1221 30 40 50 60 70 9.5 9.6 9.7 9.8 9.9 10.0 Serum HDL 554-1221 30 35 40 45 50 55 60 65 70
    36. 36. Analyte Correlations r = 0.15 Example of Correlation near zero 2 4 6 8 10 14 15 16 17 18 19 20 Animal Number 1 2 3 4 5 6 7 8 9 10 5.01 5.14 5.26 5.38 5.51 5.63 5.75 Serum HDL mRNA 1417384_at 14 15 16 17 18 19 20 5.0 5.2 5.4 5.6 Serum HDL 1417384_at
    37. 37. “ Known ” Networks vs. Observed Correlations A schematic view of the simplified Calvin cycle with subsequent sucrose phosphate synthase in the cytoplasm. Pair-wise metabolite correlations obtained numerically from the model depicted to the left. All concentrations are given in arbitrary units. K. Morgenthal, W. Weckwerth, R. Steuer, BioSystems 83 (2006) 108-117
    38. 38. Correlation Networks™ <ul><li>Integrative approach that takes advantage of analyte co-variance </li></ul><ul><li>Multiple Correlation metrics and error correcting procedures </li></ul><ul><li>Different experimental states are arranged on the same network structure for rapid comparison </li></ul><ul><li>Layout control and filters partition large networks into manageable sub networks </li></ul>
    39. 39. What’s in a Correlation? <ul><li>Central Dogma: </li></ul><ul><ul><li>Transcript to Protein/Peptide </li></ul></ul><ul><li>Metabolism: </li></ul><ul><ul><li>Reaction </li></ul></ul><ul><li>Signal Transduction: </li></ul><ul><ul><li>Protein Binding </li></ul></ul><ul><li>Regulatory Networks </li></ul><ul><ul><li>Transcription factors </li></ul></ul><ul><li>Ontology: </li></ul><ul><ul><li>Part of a Set </li></ul></ul><ul><li>Unstructured: </li></ul><ul><ul><li>Represented in Literature </li></ul></ul>Relationships built using normalized intensity values within or across treatment groups (states)
    40. 40. Network Explosion: Partitioning Complex Networks <ul><li>Correlation Networks can be very large </li></ul><ul><ul><li>(20,000 genes + 1000 proteins + 500 metabolites x 3 tissues x 3 methods x 3 states) 2 ≈ 3.37E+11 correlations </li></ul></ul><ul><ul><li>Filter on distributions and other key factors: </li></ul></ul><ul><li>Graph Properties </li></ul><ul><ul><li>Node Degree </li></ul></ul><ul><ul><li>Clustering Coefficient </li></ul></ul><ul><ul><li>Network Motifs </li></ul></ul><ul><li>Treatment Group/State Changes </li></ul><ul><ul><li>Healthy vs. Disease </li></ul></ul><ul><ul><li>Diseased vs. Diseased + Drug </li></ul></ul><ul><li>Compartment Interfaces </li></ul><ul><ul><li>Plasma/Tissue Interface </li></ul></ul><ul><li>A priori Biological Knowledge </li></ul><ul><ul><li>Neighborhoods Around Known Markers </li></ul></ul>~3400 Analytes (Nodes) ~17000 Significant Correlations (Edges)
    41. 41. Correlation Networks™: Liver - Plasma Sub-Network Plasma Liver
    42. 42. Successful Experiment (hopefully)—Now What? <ul><li>Cross-omics biomarker experiments generate TBs of data: </li></ul><ul><ul><li>Raw data, Normalized QA/QC-ed data </li></ul></ul><ul><ul><li>Statistical data (analytes-to-outcomes, Univariate & Multivariate Settings) </li></ul></ul><ul><ul><li>Data associations </li></ul></ul><ul><ul><li>Pathway, network, literature mapping </li></ul></ul><ul><li>How do you: </li></ul><ul><ul><li>Simplify this for the boss? </li></ul></ul><ul><ul><li>Share results with collaborators? Expand on these results? </li></ul></ul><ul><ul><li>Retain an audit trail for findings, manuscripts, filings? </li></ul></ul>
    43. 43. Can Semantic Approaches Help? MIT’s Exhibit <ul><li>Lightweight structured data (RDF) web publishing framework </li></ul><ul><ul><li>Part of the Simile Project at MIT’s AI Labs </li></ul></ul><ul><li>Allows for faceted browsing on top of disparate, aggregated content for small/medium numbers of objects </li></ul><ul><li>Allows for export to multiple formats and is highly customizable </li></ul>
    44. 44. The HRP Consortium & BioImage Semantic Web Model (BISM) <ul><li>The HRP Initiative is a joint R&D effort to advance the understanding, recognition and management of high-risk plaque for the benefit of all stakeholders in the healthcare system </li></ul><ul><li>Data and knowledge exchange is challenging in this environment </li></ul><ul><li>Following the Exhibit model, BGM, HRP and Clinical Semantics Group built a client-side application for inspection of BioImage Data, the BioImage Semantic Model </li></ul><ul><ul><li>Data in the model included clinical chemistries, ELISAs, patient demographics, clinical measurements and is governed by a patient-centric ontology </li></ul></ul><ul><li>The model is available in a secure environment for HRP consortium members as a web application, BioImage Semantic Web (BISM) </li></ul><ul><li>New features for viewing group statistics and establishing novel cohorts </li></ul>
    45. 45. BioImage Semantic Web (BISW)
    46. 46. It’s the Narrative… <ul><li>Optimized project planning, aligning statistics and informatics with objectives and communicating results with flexible, open-standard approaches are the key to successfully adopting and utilizing biomarkers </li></ul><ul><li>These approaches allow you to reach across data/knowledge silos within and outside of your organization </li></ul><ul><li>Many of the tools already exist but are only now being adopted in large organizations and consortiums </li></ul><ul><li>The impact of cloud computing, crowd-sourcing and the need to generate incentive for content providers to participate will determine how far narratives can be built and shared </li></ul>
    47. 47. Acknowledgements <ul><li>Research & Development: </li></ul><ul><ul><li>Neal Gordon, Wade Hines, Peter Juhasz, Jennifer Campbell, Moira Lynch, Shelagh Booth </li></ul></ul><ul><li>Informatics: </li></ul><ul><ul><li>Raul Diaz, Leijun Song, Mahesh Kulkarni, Yan Vigneault </li></ul></ul><ul><li>Statistics: </li></ul><ul><ul><li>Aram Adourian, Yu Guo, Xiaohong Li </li></ul></ul><ul><li>Business: </li></ul><ul><ul><li>Rene Myers, Pieter Muntendam </li></ul></ul>

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