woser	  
Amsterdam	                  Paris	  
Hypothe2cal	  contour	  map	  
PATIENT ADVOCATES
Sage Bionetworks  A non-profit organization with a vision to enable networked team         approaches to building better m...
Networked Approaches within a Commons           BioMedicine Information Commons                                           ...
Existing Barriers to                                                      2	                                              ...
COMPONENTS	  NEEDED	  FOR	  NETWORKED	  APPROCHES	  TO	  	  BUILDING	  EVOLVING	  MODELS	  OF	  DISEASE:	  	  RESEARCH	  2...
Two approaches to building common             scientific and technical knowledge                                        Ev...
Synapse is GitHub for Biomedical Data                                                   Every code change versioned       ...
sage bionetworks synapse project                  Watch What I Do, Not What I Say
sage bionetworks synapse project           Most of the People You Need to Work with Don’t Work with You
Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
COMPONENTS	  NEEDED	  FOR	  NETWORKED	  APPROCHES	  TO	  	       BUILDING	  EVOLVING	  MODELS	  OF	  DISEASE:	  	  RESEARC...
(Nolan	  and	  Haussler)	  
sage federation:model of biological age                                                        Faster Aging        Predict...
COMPONENTS	  NEEDED	  FOR	  NETWORKED	  APPROCHES	  TO	  	  BUILDING	  EVOLVING	  MODELS	  OF	  DISEASE:	  	  RESEARCH	  2...
weconsent.us	  
COMPONENTS	  NEEDED	  FOR	  NETWORKED	  APPROCHES	  TO	  	                                              BUILDING	  EVOLVIN...
DEMOCRATIZATION OF MEDICINE	                                CLINICAL                              INFORMATION	            ...
Crowdsourcing	  projects	  to	  build	  models	  of	  disease	  through	  use	  of	  Challenges	  hosts	  in	  the	  cloud...
Novel aspects of our competitionsTransparency,	                                                               Valida8on	  ...
REAL NAMES DISCOVERY PROJECT  LONGITUDINAL COHORT STUDY        PatientsLikeMe         ParkinsonNet       Sage BionetworksR...
THE MELANOMA HUNT	     Melanoma CROWD-SOURCING PROJECT	            www.melanomahunt.org	                              Char...
CONTEXT	•  Melanoma   is one of the most life-threatening forms of cancer	•  A difficult clinical question is whether a sus...
OBJECTIVES	•  To    capture image features of skin lesions that are predictive of melanoma (to improve the diagnosis of ma...
1                         2              3                                   Computer                                    V...
USER INTERFACE•  Presentsimages and allows the user to modify them with the image adjustment tools and captures each trial...
UNIQUE 	OPPORTUNITY 	TO EDUCATE 	  A BROAD 	 ENGAGED 	 AUDIENCE
DISTRIBUTED THINKING TOOL•    Enables the use of volunteers on the Internet to perform tasks that require human     intell...
IMAGE ADJUSTMENT TOOL	•  Basedon tools offered in GraphicsMagick with python or java interface: www.graphicsmagick.org/ 	 ...
SCIENTIFIC CHALLENGES•  Thescientific challenges will create a community-based effort to provide an unbiased assessment of ...
SCIENTIFIC CHALLENGES•  Synapse  will enable transparent, reproducible model  building and analysis workflows, as well as t...
DATA DEFINITION	•  Data   are :	  •  Anonymized      images of suspicious skin lesions 	    -    collection of sets of mul...
POTENTIAL SOURCES OF DATA	•  Who   provides the data ?	 •  Citizens  and patients upload directly from mobile   applicatio...
DATA STORAGE (SYNAPSE)	•  Both images and metadata are hosted on the Synapse platform (https://synapse.sagebase.org/)	  • ...
REPRESENTATIVE EXAMPLE	   raw data stored                                   computer-aided         in Synapse             ...
STRENGTHS	•  Generationof an unlimited and tremendous database of paired images and metadata of suspicious skin lesions 	•...
OPPORTUNITIES	•  Few,      if any, applications exist that allows a user to easily generate models to predict the malignan...
Jul	             Aug	            Sep	              Oct	           Nov	                Dec	Crowdsourcing   expert	         ...
RESEARCH	  2.0	  /	  DEMOCRATIZATION	  OF	  MEDICAL	  SCIENCES	  
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
Friend NightScience 2012
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Friend NightScience 2012

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Stephen Friend, July 13, 2012. NightScience Workshop, Paris, FR

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Friend NightScience 2012

  1. 1. woser  
  2. 2. Amsterdam   Paris  
  3. 3. Hypothe2cal  contour  map  
  4. 4. PATIENT ADVOCATES
  5. 5. Sage Bionetworks A non-profit organization with a vision to enable networked team approaches to building better models of disease BIOMEDICINE INFORMATION COMMONS INCUBATORBuilding Disease Maps Data RepositoryCommons Pilots Discovery Platform Sagebase.org
  6. 6. Networked Approaches within a Commons BioMedicine Information Commons Patients/ Citizens Data Generators CURATED DATA Data TOOLS/ Analysts METHODS RAW DATA ANALYZES/ MODELS Clinicians SYNAPSE Experimentalists
  7. 7. Existing Barriers to 2   1   REWARDS  Networked Approaches USABLE   RECOGNITION   DATA   BioMedical Information Commons Patients/ Citizens Data Generators CURATED DATA Data 5   TOOLS/ 3   Analysts REWARDS   METHODS HOW  TO   FOR   RAW DISTRIBUTE   SHARING   DATA TASKS   ANALYZES/ MODELS Clinicians 4   PRIVACY   SYNAPSE Experimentalists BARRIERS  
  8. 8. COMPONENTS  NEEDED  FOR  NETWORKED  APPROCHES  TO    BUILDING  EVOLVING  MODELS  OF  DISEASE:    RESEARCH  2.0   GEEKS  AND  SCIENTISTS   SANDBOX   PLACE  TO  BUILD  MODELS                                                                                                    SYNAPSE   OF  DISEASE  
  9. 9. Two approaches to building common scientific and technical knowledge Every code change versioned Every issue trackedText summary of the completed project Every project the starting point for new workAssembled after the fact All evolving and accessible in real time Social Coding
  10. 10. Synapse is GitHub for Biomedical Data Every code change versioned Every issue trackedData and code versioned Every project the starting point for new workAnalysis history captured in real time All evolving and accessible in real timeWork anywhere, and share the results with anyone Social CodingSocial Science
  11. 11. sage bionetworks synapse project Watch What I Do, Not What I Say
  12. 12. sage bionetworks synapse project Most of the People You Need to Work with Don’t Work with You
  13. 13. Data Analysis with SynapseRun Any ToolOn Any PlatformRecord in SynapseShare with Anyone
  14. 14. COMPONENTS  NEEDED  FOR  NETWORKED  APPROCHES  TO     BUILDING  EVOLVING  MODELS  OF  DISEASE:    RESEARCH  2.0   SETS  RULES  FOR  SHARING   DATA                                                                    THE  FEDERATION   ALLOWS  INTERLAB     DYNAMIC  RELATIONS   GEEKS   AND  SCIENTISTS   SANDBOX                                SYNAPSE   PLACE  TO  BUILD  MODELS   OF  DISEASE  
  15. 15. (Nolan  and  Haussler)  
  16. 16. 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)  
  17. 17. COMPONENTS  NEEDED  FOR  NETWORKED  APPROCHES  TO    BUILDING  EVOLVING  MODELS  OF  DISEASE:    RESEARCH  2.0             ALLOWS  PATIENT  TO  REQUEST  DATA  BACK        PORTABLE          LEGAL     GIVES  CONTROL  OF  DATA  TO  PATIENT        CONSENT   WHO  CAN  THEN  SAY  I  WANT  TO  SHARE  IT   SETS  RULES  FOR  SHARING  DATA                                    THE  FEDERATION   ALLOWS  INTERLAB     DYNAMIC  RELATIONS   GEEKS  AND  SCIENTISTS   SANDBOX                                          SYNAPSE   PLACE  TO  BUILD  MODELS   OF  DISEASE  
  18. 18. weconsent.us  
  19. 19. COMPONENTS  NEEDED  FOR  NETWORKED  APPROCHES  TO     BUILDING  EVOLVING  MODELS  OF  DISEASE:    RESEARCH  2.0   INCLUDING  CITIZENS:  DEMOCRATIZATION  OF  MEDICINE     SETS  RULES  FOR  SHARING  DATA                                                          THE  FEDERATION   ALLOWS  INTERLAB     DYNAMIC  RELATIONS   GEEKS  AND  SCIENTISTS   SANDBOX                                                SYNAPSE   PLACE  TO  BUILD  MODELS   OF  DISEASE           ALLOWS  PATIENT  TO  REQUEST  DATA  BACK        PORTABLE            LEGAL   GIVES  CONTROL  OF  DATA  TO  PATIENT   WHO  CAN  THEN  SAY  I  WANT  TO  SHARE  IT   CONSENT   ENGAGES  CITIZENS  AS  PARTNERS                                                               PATIENTS,  RESEARCHERS,  FUNDERS   BRIDGE  
  20. 20. DEMOCRATIZATION OF MEDICINE CLINICAL INFORMATION MOLECULAR DATA RESEARCH RESOURCES (Social Value Chain) ASHOKA
  21. 21. Crowdsourcing  projects  to  build  models  of  disease  through  use  of  Challenges  hosts  in  the  cloud  found  on  websites  for  Synapse  and  BRIDGE    
  22. 22. Novel aspects of our competitionsTransparency,   Valida8on  in  novel  reproducibility   -./#++0%(* 1%/2* (34#* 53,6%(* !7(%,2/* dataset   1%/2* 53,6%(* !7(%,2/* -./#++0%(* (34#* -./#++0%(* -./#++0%(* !7(%,2/* !7(%,2/* 1%/2* 1%/2* (34#* (34#* 53,6%(* 53,6%(* !#80)69*%8:* ;(#6%(* !#$%#()* ++++(,*Publica8on  in  Science   Dona8on  of  Google-­‐Transla8onal  Medicine   scale  compute  space.   sign  up  at  synapse.sagebase.org   Organiza8on  of  drug  sensi8vity  compe88ons  to  follow.  
  23. 23. REAL NAMES DISCOVERY PROJECT LONGITUDINAL COHORT STUDY PatientsLikeMe ParkinsonNet Sage BionetworksRESEARCH 2.0
  24. 24. THE MELANOMA HUNT Melanoma CROWD-SOURCING PROJECT www.melanomahunt.org Charles Ferté and Andrew Trister Sage Bionetworks
  25. 25. CONTEXT •  Melanoma is one of the most life-threatening forms of cancer •  A difficult clinical question is whether a suspicious skin lesion represents a melanoma or a benign process •  The ABCDE mnemonic and the Ugly Duckling are the current standard approaches to describe suspicious skin lesions, assign risk and decide further workup (a biopsy is eventually performed) •  Advances in computer-aided image manipulation and in scientific crowd-sourcing (e.g. Foldit, EteRNA: hundreds of thousands contributors Nature journal papers) could improve the assessment of skin lesions
  26. 26. OBJECTIVES •  To capture image features of skin lesions that are predictive of melanoma (to improve the diagnosis of malignant lesions) with an emphasis on sets of multiple lesions per patient over time •  To describe associations between quantitative imaging characteristics of skin lesions  and clinical, molecular and pathological traits in melanoma •  Toeducate the public on risks of melanoma and methods of prevention and early detection
  27. 27. 1 2 3 Computer Vision supe r co ntrib utor s Challenges data input user powered interface by Synapse butor s c ontrisingle ABCDE Ugly Duckling
  28. 28. USER INTERFACE•  Presentsimages and allows the user to modify them with the image adjustment tools and captures each trial •  Incentives and performance assessments are provided (gamification and adaptive replication) •  Empowers the user to complete jobs and participate in challenges •  Will be accessible on web and mobile devices
  29. 29. UNIQUE OPPORTUNITY TO EDUCATE A BROAD ENGAGED AUDIENCE
  30. 30. DISTRIBUTED THINKING TOOL•  Enables the use of volunteers on the Internet to perform tasks that require human intelligence, knowledge, or cognitive skills (e.g. Stardust@home, GalaxyZoo, and Amazons Mechanical Turk) •  Provides adaptive replication •  Some users do the same job, only better and subsequently are given harder jobs •  Some experts do more sophisticated jobs and are given even harder jobs •  Simple, powerful, and open source tools already exist (e.g. Bossa) •  Applied to MELANOMA HUNT, participants are invited to perform both ABCDE and Ugly Duckling scoring of skin lesions
  31. 31. IMAGE ADJUSTMENT TOOL •  Basedon tools offered in GraphicsMagick with python or java interface: www.graphicsmagick.org/ •  Quantitative transformation of the images is trivial •  Easilyadaptable to gamification solutions (e.g. using adaptive replication open-source platforms) through Python API •  Open source and distributed with MIT style license
  32. 32. SCIENTIFIC CHALLENGES•  Thescientific challenges will create a community-based effort to provide an unbiased assessment of models and methodologies for the prediction of melanoma. •  Imaging feature sets are translated in quantitative variables (numeric or categorical) •  A common dataset will be provided to all participants, with a validation dataset held out for model evaluation
  33. 33. SCIENTIFIC CHALLENGES•  Synapse will enable transparent, reproducible model building and analysis workflows, as well as the sharing of data, tools, and models with the Scientific Challenges community •  Participants can apply their best ideas in a high performance compute environment •  Allmodels, including computationally intensive ones, can be shared and re-run on a common platform, enabling transparency of the process
  34. 34. DATA DEFINITION •  Data are : •  Anonymized images of suspicious skin lesions  -  collection of sets of multiple skin lesions per patient -  augmentation of the database over the time since the evolution of the lesions are also recorded •  Anonymized demographics (age, sex, race, etc.) for each patient and anonymized pathological, clinical and molecular features (TNM, Breslow score, BRAF, c-kit, etc.) for each lesion
  35. 35. POTENTIAL SOURCES OF DATA •  Who provides the data ? •  Citizens and patients upload directly from mobile applications (iOS android) and through the web •  Medical research institutions and cooperative groups (e.g. International Dermoscopy Society, etc)
  36. 36. DATA STORAGE (SYNAPSE) •  Both images and metadata are hosted on the Synapse platform (https://synapse.sagebase.org/) •  Synapse is a collaborative compute space that allows scientists to share and analyze data together •  Synapse allows for both public and private projects •  Synapse enables the data to be directly loaded into analytical tools like R and then to store the analysis
  37. 37. REPRESENTATIVE EXAMPLE raw data stored computer-aided in Synapse image(single or multiple transformation images of skin lesions) quantitative model building and feature correlation with extraction endpoint User performance: 87.5%
  38. 38. STRENGTHS •  Generationof an unlimited and tremendous database of paired images and metadata of suspicious skin lesions •  Crowd-sourcing will facilitate a community of citizen/patients interested in an important public health concern •  Innovation combining recent advances in image adjustment, crowd-sourcing and predictive modeling
  39. 39. OPPORTUNITIES •  Few, if any, applications exist that allows a user to easily generate models to predict the malignancy of a skin lesion •  Engagingcitizen/patients to directly provide a complete record of multiple skin lesions over the entire body which can be tracked over time will generate an unparalleled dataset •  Brute-force of crowd-sourcing both to populate a database and to generate predictive models in an unlimited manner
  40. 40. Jul Aug Sep Oct Nov Dec Crowdsourcing expert Engage experts Obtain data from experts Python client for Synapse Engage development partners Image adjustment tool Mobile/web interface Adaptive replication for images Key PLC Data Collaboration Py client for deID Development Potential Congress Engage strategic partners 54
  41. 41. RESEARCH  2.0  /  DEMOCRATIZATION  OF  MEDICAL  SCIENCES  

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