Stephen Friend SciLife 2011-09-20

440 views

Published on

Stephen Friend, Sept 20, 2011. SciLife, Stockholm, Sweden

Published in: Health & Medicine
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
440
On SlideShare
0
From Embeds
0
Number of Embeds
2
Actions
Shares
0
Downloads
4
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Stephen Friend SciLife 2011-09-20

  1. 1. Integrating layers of omics data models and computespaces needed to build a “Convivial Knowledge Expert” Use of Bionetworks to Build Maps of Diseases Moving beyond the linear Stephen Friend MD PhD Sage Bionetworks (Non-Profit Organization) Seattle/ Beijing/ Amsterdam SciLife September 21st, 2011
  2. 2. why consider the fourth paradigm- data intensive science thinking beyond the narrative, beyond pathways advantages of an open innovation compute space it is more about how than what
  3. 3. Alzheimer’s Diabetes Treating Symptoms v.s. Modifying DiseasesCancer Obesity Will it work for me? Biomarkers?
  4. 4. Familiar but Incomplete
  5. 5. Reality: Overlapping Pathways
  6. 6. WHY NOT USE “DATA INTENSIVE” SCIENCETO BUILD BETTER DISEASE MAPS?
  7. 7. “Data Intensive Science”- “Fourth Scientific Paradigm”For building: “Better Maps of Human Disease” Equipment capable of generating massive amounts of data IT Interoperability Open Information System Evolving Models hosted in a Compute Space- Knowledge Expert
  8. 8. It is now possible to carry out comprehensive monitoring of many traits at the population levelMonitor disease and molecular traits in populations Putative causal gene Disease trait
  9. 9. what will it take to understand disease? DNA RNA PROTEIN (dark matter)MOVING BEYOND ALTERED COMPONENT LISTS
  10. 10. 2002 Can one build a “causal” model?
  11. 11. How is genomic data used to understand biology? RNA amplification Tumors Microarray hybirdization Tumors Gene Index Standard GWAS Approaches Profiling Approaches Identifies Causative DNA Variation but Genome scale profiling provide correlates of disease provides NO mechanism   Many examples BUT what is cause and effect?   Provide unbiased view of molecular physiology as it relates to disease phenotypes trait   Insights on mechanism   Provide causal relationships and allows predictions Integrated Genetics Approaches
  12. 12. Integration of Genotypic, Gene Expression & Trait Data Schadt et al. Nature Genetics 37: 710 (2005) Millstein et al. BMC Genetics 10: 23 (2009) Causal Inference “Global Coherent Datasets” •  population based •  100s-1000s individuals Chen et al. Nature 452:429 (2008) Zhu et al. Cytogenet Genome Res. 105:363 (2004) Zhang & Horvath. Stat.Appl.Genet.Mol.Biol. 4: article 17 (2005) Zhu et al. PLoS Comput. Biol. 3: e69 (2007)
  13. 13. Constructing Co-expression Networks Start with expression measures for genes most variant genes across 100s ++ samples 1 2 3 4 Note: NOT a gene expression heatmap 1 1 0.8 0.2 -0.8 Establish a 2D correlation matrix 2 for all gene pairsexpression 0.8 1 0.1 -0.6 3 0.2 0.1 1 -0.1 4 -0.8 -0.6 -0.1 1 Brain sample Correlation Matrix Define Threshold eg >0.6 for edge 1 2 4 3 1 2 3 4 1 1 1 4 1 1 1 0 1 1 0 1 2 2 1 1 1 0 1 1 0 1 1 1 1 0 Hierarchically 3 Identify modules 4 0 0 1 0 2 3 cluster 4 3 0 0 0 1 1 1 0 1 Network Module Clustered Connection Matrix Connection Matrix sets of genes for which many pairs interact (relative to the total number of pairs in that set)
  14. 14. Data integration via Bayesian Network Yeast segregants Public databases ******BYRMSynthetic complete Protein- medium protein Logorithm growth interations Transcription factor binding Gene expression sites genotypes Yeast segregants Protein Metabolite interations Bayesian network Courtesy of Dr. Jun Zhu
  15. 15. Preliminary Probabalistic Models- Rosetta /Schadt Networks facilitate direct identification of genes that are causal for disease Evolutionarily tolerated weak spots Gene symbol Gene name Variance of OFPM Mouse Source explained by gene model expression* Zfp90 Zinc finger protein 90 68% tg Constructed using BAC transgenics Gas7 Growth arrest specific 7 68% tg Constructed using BAC transgenics Gpx3 Glutathione peroxidase 3 61% tg Provided by Prof. Oleg Mirochnitchenko (University of Medicine and Dentistry at New Jersey, NJ) [12] Lactb Lactamase beta 52% tg Constructed using BAC transgenics Me1 Malic enzyme 1 52% ko Naturally occurring KO Gyk Glycerol kinase 46% ko Provided by Dr. Katrina Dipple (UCLA) [13] Lpl Lipoprotein lipase 46% ko Provided by Dr. Ira Goldberg (Columbia University, NY) [11] C3ar1 Complement component 46% ko Purchased from Deltagen, CA 3a receptor 1 Tgfbr2 Transforming growth 39% ko Purchased from Deltagen, CANat Genet (2005) 205:370 factor beta receptor 2
  16. 16. List of Influential Papers in Network Modeling   50 network papers   http://sagebase.org/research/resources.php
  17. 17. (Eric Schadt)
  18. 18. Recognition that the benefits of bionetwork based molecularmodels of diseases are powerful but that they requiresignificant resourcesAppreciation that it will require decades of evolvingrepresentations as real complexity emerges and needs to beintegrated with therapeutic interventions
  19. 19. Sage Mission Sage Bionetworks is a non-profit organization with a vision to create a commons where integrative bionetworks are evolved by contributor scientists with a shared vision to accelerate the elimination of human diseaseBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform Sagebase.org
  20. 20. Sage Bionetworks Collaborators  Pharma Partners   Merck, Pfizer, Takeda, Astra Zeneca, Amgen, Johnson &Johnson  Foundations   Kauffman CHDI, Gates Foundation  Government   NIH, LSDF  Academic   Levy (Framingham)   Rosengren (Lund)   Krauss (CHORI)  Federation   Ideker, Califarno, Butte, Schadt 22
  21. 21. Engaging Communities of Interest NEW MAPS Disease Map and Tool Users- ( Scientists, Industry, Foundations, Regulators...) PLATFORM Sage Platform and Infrastructure Builders- ( Academic Biotech and Industry IT Partners...) RULES AND GOVERNANCE Data Sharing Barrier Breakers- (Patients Advocates, Governance ORM and Policy Makers,  Funders...)M APS F NEW TOOLS PLAT NEW Data Tool and Disease Map Generators- (Global coherent data sets, Cytoscape, RULES GOVERN Clinical Trialists, Industrial Trialists, CROs…) PILOTS= PROJECTS FOR COMMONS Data Sharing Commons Pilots- (Federation, CCSB, Inspire2Live....)
  22. 22. Example 1: Breast Cancer Coexpression Networks Module combination Partition BN Bayesian NetworkSurvival Analysis 24 Zhang B et al., manuscript
  23. 23. Generation of Co-expression & Bayesian Networks frompublished Breast Cancer Studies 4 Public Breast Cancer Datasets NKI: van de Vijver et al. A gene-expression signature as a predictor of survival in breast cancer. N Engl J Med. 2002 Dec 19;347 295 samples (25):1999-2009. Wang Y et al. Gene-expression profiles to predict distant metastasis of lymph-node- negative primary breast cancer. Lancet. 286 samples 2005 Feb 19-25;365(9460):671-9. Miller: Pawitan Y et al. Gene expression profiling spares early breast cancer patients from adjuvant therapy: derived and 159 samples validated in two population-based cohorts. Breast Cancer Res. 2005;7(6):R953-64. Christos: Sotiriou C et al.. Gene expression profiling in breast cancer: understanding the molecular basis of 189 samples histologic grade to improve prognosis. J Natl Cancer Inst. 2006 Feb 15;98(4): 262-72. 25
  24. 24. Recovery  of  EGFR  and  Her2  oncoproteins  downstream  pathways  by  super  modules  
  25. 25. Comparison  of  Super-­‐modules  with  EGFR  and  Her2   signaling  and  resistance  pathways  
  26. 26. Key  Driver  Analysis  •  IdenDfy  key  regulators  for  a  list  of  genes  h    and  a  network  N  •  Check  the  enrichment  of  h in  the  downstream  of  each  node  in  N  •  The  nodes  significantly  enriched  for  h  are  the  candidate  drivers   28
  27. 27. A) Cell Cycle (blue) B) Chromatin modification (black)C) Pre-mRNA Processing (brown) D) mRNA Processing (red) Global driver Global driver & RNAi validation 29
  28. 28. Signaling between Super Modules (View Poster presented by Bin Zhang)
  29. 29. Example: The Sage Non-Responder Project in Cancer •  To identify Non-Responders to approved drug regimens so Purpose: we can improve outcomes, spare patients unnecessary toxicities from treatments that have no benefit to them, and reduce healthcare costs Leadership: •  Co-Chairs Stephen Friend, Todd Golub, Charles Sawyers Gary Nolan & Rich Schilsky Initial •  AML (at first relapse)- Jerry Radich Studies: •  Non-Small Cell Lung Cancer- Roy Herbst •  Ovarian Cancer (at first relapse)- Beth Karlan •  Breast Cancer- Dan Hayes •  Renal Cell- Rob MoetzerSage Bionetworks • Non-Responder Project
  30. 30. AndersNew Type II Diabetes Disease Models Rosengren Global expression data 340 genes in islet-specific from 64 human islet donors open chromatin regions Blue module: 3000 genes Associated with Type 2 diabetes Elevated HbA1c Reduced insulin secretion 168 overlapping genes, which have •  Higher connectivity •  Markedly stronger association with •  Type 2 diabetes •  Elevated HbA1c •  Reduced insulin secretion •  Enrichment for beta-cell transcription factors and exocytotic proteins
  31. 31. New Type II Diabetes Disease Models Anders Rosengren•  Search across 1300 datasets in MetaGEO at Sage for similar expression profiles Top hit: Islet dedifferentiation study where the 168 genes were upregulated in mature islets and downregulated in dedifferentiated islets (Kutlu et al., Phys Gen 2009)•  Analyses of expression-SNPs and clinical SNPs as well as Causal Inference Test•  Identification of candidate key genes affecting beta-cell differentiation and chromatinWorking hypothesis:Normal beta-cell: open chromatin in islet-specific regions,high expression of beta-cell transcription factors,differentiated beta-cells and normal insulin secretionDiabetic beta-cell: lower expression of beta-cell transcriptionfactors affecting the identified module, dedifferentiation,reduced insulin secretion and hyperglycemiaNext steps: Validation of hypothesis and suggested key genes in human islets
  32. 32. Probing  complex  biology    •  The  more  we  learn  about  it,  the  more   complicated  it  becomes!  •  Cancers  geneDc  fingerprints  are  highly   diverse;  most  mutaDons  were  unique  to   individual    •  How  to  piece  everything  together?  
  33. 33. perturba)ons   observa)ons  to  models   perturba)ons   diseases  
  34. 34. A  framework  for  data  integraDon knowledge   Medline Biocarta/Biopathway BiologistsHigh  throughput  data   Microarray data probabilistic Database Proteomic data graphic models Metabolomic data Genomics Hypothesis, test GUI Genetics
  35. 35. Yeast  segregants   Public     databases   ******BYRMSynthe)c  complete     Protein-­‐medium   protein   intera)ons  Logorithm  growth   Transcrip)on   Gene  expression   metabolites   genotypes   factor  binding   sites   Protein   Yeast  segregants   Metabolite   intera)ons   Bayesian  network  
  36. 36. Bayesian  network:  Incorpora)ng  TFBS  and  PPI  data  as  a   scale-­‐free  network  prior  •  DNA-­‐protein  binding  data   –  Knowledge  of  what  proteins  regulate  transcripDon  of  a  given   gene  
  37. 37. PPI:  Can  we  find  informaDon  overlapped   with  gene  expressions?   4-­‐clique   4-­‐clique   3-­‐clique   3-­‐clique   Clique  community   (par)al  clique)   Zhu  J  et  al,  Nature  GeneDcs,    2008  
  38. 38. IntegraDng  transcripDon  factor  (TF)  binding   data  and  PPI  •  Introducing  scale-­‐free  priors  for  TF  and  large   PPI  complex  •  Fixed  prior  for  small  PPI  complex   Zhu  J  et  al,  Nature  GeneDcs,  2008  
  39. 39. Integra)on  improves  network  quali)es  BN KO data GO terms TF data •  The  pair  is  independent  w/o any priors 125 55 26w/ genetics priors 139 59 34w/ genetics, TF and PPI priors 152 66 52 •  The  pair  is  causa/reacDve  Zhu  et  al.,  Cytogene)cs,  2004  Zhu  et  al.  PLoS  Comp  Biol.  2007  Zhu  J  et  al.,  Nature  Gene)cs,  2008  
  40. 40. ProspecDve  validaDon  is  the  gold   standard   ILV6  gives  rise  to  large  expression  signature   GCN4   •     ILV6  KO  sig  enriched  (p~10E-­‐52)   •     GCN4  upregulated  in  ILV6  KO    large  signature   ILV6   LEU2  KO  gives  rise  to  small  expression  signature   •     LEU2  KO  sig  enriched  (p~10E-­‐18)   •     GCN4  downregulated  in  LEU2  KO    small  signature   LEU2  GCN4   Zhu  J  et  al.,  Nature  Gene)cs,  2008  
  41. 41. Lung  Cancer  Bayesian  network  •  Built  from  240  lung  cancer  samples  •  7785  genes  •  10642  links  
  42. 42. EMT’s proteomic signatures epithelial mesenchymalCell medium signature Cell extract signature Cell surface signature
  43. 43. Direct  overlaps  of  EMT  proteomic   signatures   extract surface media extract 267 75 57 surface 31% 240 52 media 27% 25% 208
  44. 44. Analysis  of  EMT  signatures  through     lung  cancer  network  CellmediumsignatureCell extractsignature overlapsCell surfacesignature De novo constructed lungsignatures cancer regulatory network subnetworks
  45. 45. Hallmark  features  of  EMT  •  Decrease of E-cadherin and increase of Vimentin (criteria used in defining signatures) –  CTNNA1 is in all three proteomic signatures; •  There are 6 nodes connected to CTNNA1 in the lung cancer network including ARF1 –  VIM is in all three proteomic signatures •  There are 7 nodes connected to VIM in the lung cancer network including NOTCH2, EMP3
  46. 46. Lung  Network  Conclusions  •  All  proteomic  signatures  are  coherently  co-­‐ regulated  at  transcripDon  level;  •  GOBP  annotaDons  for  signatures  in  cell  surface,   condiDoned  media  fracDons  are  expected;  •  Lipid  metabolism  for  signatures  in  the  total  cell   extract  is  also  expected.  •  Subnetworks  for  EMT  proteomic  signatures   contain  all  known  hallmark  features  of  EMT;  •  These  subnetworks  can  provide  beger  context  to   understand  EMT  and  to  idenDfy  key  regulators  of   EMT.    
  47. 47. Can we accelerate the pace of scientific discovery? 2008   2009   2010   2011  
  48. 48. How is the Federation differentfrom a “traditional” collaboration? collaboration 2.0
  49. 49. Rules of the game:transparency & trust   Shared data tools models and prepublications   Conflict of interests   Intellectual property   Authorship
  50. 50. sage bionetworks synapse project Watch What I Do, Not What I Say Reduce, Reuse, Recycle My Other Computer is Amazon Most of the People You Need to Work with Don’t Work with You
  51. 51. sage federation:scientific pilot projects   Type-II diabetes   Warburg effect in cancer   Human aging Cellular  mortality  (aging)   Cellular  immortality  (cancer)  
  52. 52. sage federation:warburg effect project
  53. 53. warburg effect projectinterconnected discovery bioinformatic bioinformatic confirmation that metabolic coherent taylor, genes implicated in the warburg effect are prostate sawyers, et differentially expressed across a wide range dataset al., mskcc! of cancer types! butte lab layer in! additional ! tissue types! network dynamics prostate network modeling generate an aerobic glycolysis signature and ʻreverse engineerʼ master transcription factor genes associated with poor prognosis in breast regulators of the warburg effect transcriptional prostate cancer are disproportionately found program ! amongst networks regulating glycolysis genes! colon lung renal brain b cell califano lab sage bionetworks
  54. 54. warburg effect projectso what? discovery integration to translation, a validation path peter nelson! pre-clinical target validation! from thompson, science, 2009! jim olson! from sawyers, pcctc presentation! target identification! clinical validation! lists of nodes and key drivers!
  55. 55. sage federation:human aging project JusDn  Guinney   Stephen  Friend*   Mariano  Alvarez   Celine  Lefebrev   Greg  Hannum   Andrea  Califano*   Januz  Dutkowski   Trey  Ideker*   Kang  Zhang*  
  56. 56. sage federation:what is the impact of disease/environment on “biological age” ? Biological  Age   2009   2001   Chronological  Age  
  57. 57. sage federation:model of biological age Faster Aging Predicted  Age  (liver  expression)   Slower Aging Clinical Association -  Gender -  BMI -  Disease Age Differential Genotype Association Gene Pathway Expression Chronological  Age  (years)  
  58. 58. human aging: clinical associations with differential aging – gene expression in human liver Faster  Aging   ! 20 !Bioage  Difference   10 (years)   0 Slower  Aging   −10 ! ! Female   FALSE TRUE Underweight   Fit   Overweight   Male  
  59. 59. human aging:predicting bioage using whole blood methylation•  Independent training (n=170) and validation (n=123) Caucasian cohorts•  450k Illumina methylation array•  Exom sequencing•  Clinical phenotypes: Type II diabetes, BMI, gender… Training Cohort: San Diego (n=170) Validation Cohort: Utah (n=123) RMSE=3.35 RMSE=5.44 100 100 ! ! !! ! ! ! !! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! !!!! !! ! ! ! ! ! !! ! ! !! ! ! !! ! ! !! ! 80 Biological Age Biological Age !! ! !!!! !! ! !! ! ! ! ! !!! ! 80 ! !!! !! ! ! ! ! !! ! ! ! ! ! !! ! ! ! ! !!!! !!! ! !!! !! ! !! !!!! ! !!! !! !! ! ! ! ! ! ! ! ! ! ! !!! ! ! !! ! ! !! ! !! !! ! !! ! !! ! ! !!!! ! ! ! ! !! ! !! ! ! ! !! ! ! !!! ! ! ! !!! ! !! !! !! ! !! ! ! ! ! !! ! ! ! ! !! !! ! ! ! ! 60 !!! ! ! ! ! !! ! ! ! ! ! !! ! 60 ! ! ! !! !! ! ! ! ! ! !! ! !!!! ! ! ! ! ! !! ! ! !! ! ! ! !! ! ! ! ! ! ! 40 40 ! ! 40 50 60 70 80 90 100 40 50 60 70 80 90 Chronological Age Chronological Age
  60. 60. human aging:clinical associations with differential bioage BMI vs Diff Bioage Diabetes vs Diff Bioage p=0.0362 p=0.000466 10 10 ! ! Train:  San  Diego   ! ! !! ! ! ! ! ! 5 5 ! !! !!! ! ! ! ! ! !! ! ! Diff Bioage Diff Bioage ! !!! ! ! ! !! ! ! !! ! ! ! ! !!! ! !! ! !!! ! ! !!! !! ! !! ! ! ! ! ! ! !! ! ! ! ! !! ! ! ! ! 0 0 ! !!! !!! ! !!! ! ! ! ! ! ! !!! ! ! ! ! ! ! ! ! ! ! !! !! !! !!! ! ! ! !! !! ! !! ! ! ! ! ! ! ! ! !! ! ! !! !! ! ! ! !5 !5 ! !! ! ! ! ! ! ! ! !10 !10 ! ! 20 25 30 35 40 45 N Type II BMI BMI vs Diff Bioage Diabetes vs Diff Bioage p=0.00173 p=1.34e!10 ValidaDon:  Utah   !! ! !! ! ! 10 10 ! ! ! !! ! ! ! ! ! ! !! ! ! ! !! ! ! !!! ! 5 5 !! !!!! ! ! !!! ! ! ! ! Diff Bioage Diff Bioage ! !!! ! !! !! ! ! !! ! ! ! !!! !! ! ! !! !! ! ! ! ! !!! 0 0 ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! !! ! ! ! ! !! ! !5 !5 ! ! ! ! ! ! ! !15 !15 ! ! ! ! 20 25 30 35 40 45 50 N Type II BMI
  61. 61. human aging:clinical associations with combined cohorts BMI vs Diff Bioage Diabetes vs Diff Bioage Smoker vs Diff Bioage p=0.00406 p=9.7e!11 p=0.00764 Univariate Analysis 10 10 10 ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! !! 5 5 5 !! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !! ! ! !! ! ! ! ! !! ! ! ! ! !! !! ! ! !! ! ! ! ! !!! ! ! ! ! ! ! !! ! ! !! ! ! ! !! !! ! ! ! ! !! ! ! ! ! ! ! ! Diff Bioage Diff Bioage Diff Bioage !! ! ! ! ! ! ! ! ! ! ! ! !!! !!! !!! ! ! 0 0 0 ! !!! ! ! !! ! ! ! ! !! ! ! ! ! ! !! !! ! ! !! ! !! ! !!! ! ! ! !!!! ! ! ! ! ! ! ! !!! ! !! !! ! ! ! ! ! ! !! ! ! ! ! !! ! ! !!! !! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! ! ! ! ! ! !! ! ! ! ! ! ! ! ! !!!! ! !5 !5 !5 ! ! ! ! ! !!! ! ! ! ! !10 !10 !10 ! ! ! ! ! ! 20 30 40 50 N Type II FALSE TRUE BMI Multiple Regression Gender   BMI   Diabetes   Smoker   p=.45   p=.619   p=4.82e-­‐07   p=.02  
  62. 62. human aging:mechanism of biological aging stochasDc  vs  mechanisDc   deterioraDon  by  random  “hits”     systemaDc  process  
  63. 63. human aging:mechanism of aging – higher entropy? Primary Cohort Secondary Cohort 14 p=6.616e!16 p=3.528e!05 !48 12 10 !50 Entropy Entropy 8 !52 6 !54 4 !56 2 40 50 60 70 80 90 40 45 50 55 60 65 Mean age per window Mean age per window
  64. 64. human aging:research summary •  Created predictive model of biological age •  Type-II diabetes strongly correlates with accelerated aging •  Identified genetic variant that may induce methyl-driven acceleration of aging •  General mechanism of aging may involve loss of signal and increased disorder within the methylome
  65. 65. sage federationalternative model of scientific collaboration?
  66. 66. Federated  Aging  Project  :     Combining  analysis  +  narraDve     =Sweave Vignette Sage Lab R code + PDF(plots + text + code snippets) narrative HTML Data objectsCalifano Lab Ideker Lab Submitted Paper Shared  Data   JIRA:  Source  code  repository  &  wiki   Repository  
  67. 67. Why not share clinical /genomic data and model building in the ways currently used by the software industry (power of tracking workflows and versioning
  68. 68. Synapse  as  a  Github  for  building  models  of  disease  
  69. 69. Evolution of a Software Project
  70. 70. Biology Tools Support Collaboration
  71. 71. Potential Supporting TechnologiesAddama Taverna tranSMART
  72. 72. Platform for Modeling SYNAPSE  
  73. 73. INTEROPERABILITY (tranSMART)INTEROPERABILITY  
  74. 74.  TENURE      FEUDAL  STATES      
  75. 75. !Group D LEGAL STACK-ENABLING PAIENTS: John Wilbanks
  76. 76. Arch2POCM  Restructuring  Drug  Discovery  
  77. 77. why consider the fourth paradigm- data intensive science thinking beyond the narrative, beyond pathways advantages of an open innovation compute space it is more about how than what
  78. 78. Moving beyond the linear linear pathwayslinear ways of building modelslinear ways of working together
  79. 79. Convivial  ManifestaDons  of  the  Sage  Synapse  Commons   “What Technology Wants” pp 264 by Kevin Kelly
  80. 80. OPPORTUNITIES FOR THE SCILIFE COMMUNITY Data sets, Tools and Models Joining Synapse Communities Joining Federation Projects Joining Arch2POCM Change reward structures for sharing data (patients and academics)

×