data science at nih and its relationship to social computing, behavioral-cultural modeling, &...
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Data Science at NIH and its Relationship to Social Computing, Behavioral-Cultural Modeling, &
PredictionPhilip E. Bourne, PhD, FACMI
Associate Director for Data Science
National Institutes of Health
SBP15, Washington DCApril 2, 2015
[Thanks to Patty Mabry]
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DisclaimerI know little about methods and
developments in Social Computing, Behavioral-Cultural Modeling, &
Prediction
However, I do believe it is critical to NIH’s mission and am here to learn
how we can help
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Office of Biomedical Data Science
Mission Statement
To foster an open ecosystem that enables biomedical research to be
conducted as a digital enterprise that enhances health, lengthens life and reduces illness and disability & to train the next generation of data
scientistsGoals expanded from recommendations in the June 2012 DIWG and BRWWG reports.
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Let Me Give You 4 Examples of What Drives Us …
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1. We are at a Point of Disruption
Evidence:– Google car
– 3D printers
– Waze
– Robotics
– Sensors
From: The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson & Andrew McAfee
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2. DemocratizationThe Story of Meredith
http://fora.tv/2012/04/20/Congress_Unplugged_Phil_Bourne
Stephen Friend
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47/53 “landmark” publications could not be replicated
[Begley, Ellis Nature, 483, 2012] [Carole Goble]
3. Reproducability
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4. New Opportunities
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“And that’s why we’re here today. Because something called precision medicine … gives us one of the greatest opportunities for new medical breakthroughs that we have ever seen.”
President Barack ObamaJanuary 30, 2015
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Precision Medicine Initiative
Vision: Build a broad research program to encourage creative approaches to precision medicine, test them rigorously, and, ultimately, use them to build the evidence base needed to guide clinical practice.
Near Term: apply the tenets of precision medicine to a major health threat – cancer
Longer Term: generate the knowledge base necessary to move precision medicine into virtually all areas of health and disease
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Precision Medicine Initiative
National Research Cohort – >1 million U.S. volunteers
– Numerous existing cohorts (many funded by NIH)
– New volunteers
Participants will be centrally involved in design and implementation of the cohort
They will be able to share genomic data, lifestyle information, biological samples – all linked to their electronic health records
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National Research Cohort:What Early Success Might Look Like
A real test of pharmacogenomics—right drug at the right dose for the right patient
New therapeutic targets by identifying loss-of-function mutations protective against common diseases– PCSK9 for cardiovascular disease
– SLC30A8 for type 2 diabetes
Resilience – finding individuals who should be ill but aren’t
New ways to evaluate mHealth technologies for prevention/management of chronic diseases
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Precision Medicine: What Success Might Look Like
50-year-old woman with type 2 diabetes visits her doctor
Now– Though woman’s glucose control has been suboptimal,
doctor renews her prescription for drug often used for type 2 diabetes
– Continues to monitor blood glucose with fingersticks and glucometer, despite dissatisfaction with these methods
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Precision Medicine: What Success Might Look Like
50-year-old woman with type 2 diabetes visits her doctor
Future: + 2 years– Volunteers for new national research network
• Sample of her DNA, along with her health information, sent to researchers for sequencing/analysis
• Can view her health/research data via smartphone
– Agrees to researchers’ request to track her glucose levels via tiny implantable chip that sends wireless signals to her watch, researchers’ computers
• Using these data, she changes diet, medicine dose schedule
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Other Diseases: What Success Might Look Like
50-year-old woman with type 2 diabetes visits her doctor
Future: + 5 years– Receives word from her doctor about a new drug based
upon improved molecular understanding of type 2 diabetes
– When she enters drug’s name into her smartphone’s Rx app, her genomic data show she’ll metabolize the drug slowly
• Her doctor alters the dose accordingly
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Other Diseases: What Success Might Look Like
50-year-old woman with type 2 diabetes visits her doctor
Future: + 10 years– Celebrates her 60th birthday and reflects with her family
about how proud she is to be part of cohort study
– Her glucose levels remain well controlled; she’s suffered no diabetes-related complications
– Her children decide to volunteer for cohort study
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EHRsPatient Partnerships
Data Science
GenomicsTechnologies
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Example Relevant to SBP
Just because you have all these data does not mean it is useful:– What span and temporal sequence do we need to make
establish meaningful outcomes?
– What range of factors so we need to consider?
NIH BSSR-Systems Science Listserv contact Patty Mabry, NIH Office of Disease Prevention, to join: [email protected]
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The BD2K Program is Central to the Mission
Planned – Black; Available- Green
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Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:• Sustainability• Efficiency• Collaboration• Training
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Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:• Sustainability• Efficiency• Collaboration• Training
VirtuousResearch
Cycle
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Consider an example…
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Big Data: The study involved MRI images & GWAS data from over 30,000 people
Collaboration: Data came from many different sights affiliated with the ENIGMA consortium
Methods: To homogenize data from different sites, the group designed standardized protocols for image analysis, quality assessment, genetic imputation, and association
Found five novel genetic variants
Results provided insight into the variability of brain development, and may be applied to study of neuropsychiatric dysfunction
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Community – Enigma, BD2K
Policy – Improved consent methods– Cloud accessibility for human subjects data– Trusted partners– Data sharing
Infrastructure– Standards, compute resources, software
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Communities: 2014 Summary
Visioning workshop convened 9/3/14
Launched BD2K ($32M)
– 12 Centers of data excellence
– Data Discovery Index Coordination Consortium (DDICC)
– Training awards
First successful consortia meeting 11/3-4
Workshops to inform future funding
– Software indexing and discoverability
– Gaming
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Communities: 2015 Activities
New FOAs with outreach to new
communities – math, stats, comp science etc.
Work with e.g GA4GH, RDA, FORCE11,
NDS ….
IDEAS lab with NSF
Competition with international funders
Software carpentry, hackathons, Pi Day
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Communities: Questions?
Societies of the modern age?
How to enable these groups?
How to marry the funding of individuals with the funding of communities?
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Policies: Now & Forthcoming
Data Sharing– Genomic data sharing announced
– Data sharing plans on all research awards
– Data sharing plan enforcement
• Machine readable plan
• Repository requirements to include grant numbers
http://www.nih.gov/news/health/aug2014/od-27.htm
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Policies - Forthcoming
Data Citation– Goal: legitimize data as a form of scholarship
– Process:
• Machine readable standard for data citation (done)
• Endorsement of data citation for inclusion in NIH bib sketch, grants, reports, etc.
• Example formats for human readable data citations
• Slowly work into NLM/NCBI workflow
dbGaP in the cloud (done!)
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BD2KCenter
BD2KCenter
BD2KCenter
BD2KCenter
BD2KCenter
BD2KCenter
DDICC
Software
Standards
Infrastructure - The Commons
Labs
Labs
Labs
Labs
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The Commons
Digital Objects (with UIDs)
Search(indexed metadata)
Computing Platform
Th
e C
omm
ons
Vivien BonazziGeorge Komatsoulis
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The Commons: Compute Platforms
The CommonsConceptual Framework
Public CloudPlatforms
Super Computing (HPC) Platforms
Other Platforms ?
Google, AWS (Amazon)
Microsoft (Azure), IBM,
other?
In house compute
solutions
Private clouds, HPC
– Pharma
– The Broad
– Bionimbus
Traditionally low access
by NIH
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The Commons: Business Model
[George Komatsoulis]
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Infrastructure: Standards
2013 Workshop on Frameworks for Community-Based Standards
August 2014 Input on Information Resources for Data-Related Standards Widely Used in Biomedical Science – 30 responses
Feb 2015 Workshop Community-based Data and Metadata Standards
Internal CDE Registry project
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Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:• Sustainability• Efficiency• Collaboration• Training
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Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:• Sustainability• Efficiency• Collaboration• Training
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Workforce Training
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Problem: Lack of Biomedical Data Science Specialists
BD2K T32/T15
BD2K K01
Career path workshops – eg AAU
Challenge model of funding
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Problem: Limited Access to Data Science Training
BD2K R25
Metadata for training materials
Community-sourced cataloging and indexing of training opportunities
Measure utility
NIH Workforce Development Center
RFA-ES-15-004
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I not only use all the brains I have, but all I can borrow.
– Woodrow Wilson
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Associate Director for Data Science
Commons BD2K Efficiency
Sustainability Education Innovation Process
• Cloud – Data & Compute
• Search• Security • Reproducibility
Standards• App Store
• Coordinate• Hands-on• Syllabus• MOOCs
• Community• Centers• Training Grants• Catalogs• Standards• Analysis
• Data Resource Support
• Metrics• Best
Practices• Evaluation• Portfolio
Analysis
The Biomedical Research Digital Enterprise
Partnerships
Collaboration
Programmatic Theme
Deliverable
Example Features • IC’s• Researchers• Federal
Agencies• International
Partners• Computer
Scientists
Scientific Data Council External Advisory Board
Training