The document discusses the importance of data science at the NIH, highlighting its relationship with social computing and behavioral modeling to improve health outcomes. It outlines the NIH's goals, such as fostering an open ecosystem for biomedical research and developing the Precision Medicine Initiative, which aims to enhance personalized healthcare through extensive research and genomic data sharing. The document also emphasizes the need for collaboration, reproducibility, and training in biomedical data science to address contemporary health challenges.

1. Data Science at NIH and its
Relationship to Social Computing,
Behavioral-Cultural Modeling, &
Prediction
Philip E. Bourne, PhD, FACMI
Associate Director for Data Science
National Institutes of Health
SBP15, Washington DC
April 2, 2015
[Thanks to Patty Mabry]

2. Disclaimer
I 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

3. 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
scientists
Goals expanded from recommendations in the June 2012 DIWG and
BRWWG reports.

4. Let Me Give You 4 Examples of What
Drives Us …

5. 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

6. 2. Democratization
The Story of Meredith
http://fora.tv/2012/04/20/Congress_Unplugged_
Phil_Bourne
Stephen Friend

7. 47/53 “landmark” publications
could not be replicated
[Begley, Ellis Nature,
483, 2012] [Carole Goble]
3. Reproducability

8. 4. New Opportunities

9. “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 Obama
January 30, 2015

10. 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

11. 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

12. 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

13. 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

14. 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

15. 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

16. 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

17. EHRsPatient Partnerships
Data Science
GenomicsTechnologies

18. 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:
mabryp@od.nih.gov

19. The BD2K Program is Central
to the Mission
Planned – Black; Available- Green

20. Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Collaboration
• Training

21. Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Collaboration
• Training
Virtuous
Research
Cycle

22. Consider an example…

23.  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

24.  Community – Enigma, BD2K
 Policy
– Improved consent methods
– Cloud accessibility for human subjects data
– Trusted partners
– Data sharing
 Infrastructure
– Standards, compute resources, software

25. 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

26. 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

27. Communities: Questions?
 Societies of the modern age?
 How to enable these groups?
 How to marry the funding of individuals with
the funding of communities?

28. 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

29. 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!)

30. BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
BD2K
Center
DDICC
Software
Standard
s
Infrastructure - The
Commons
Labs
Labs
Labs
Labs

31. The Commons
Digital Objects
(with UIDs)
Search
(indexed metadata)
Computing
Platform
TheCommons
Vivien Bonazzi
George Komatsoulis

32. The Commons: Compute Platforms
The Commons
Conceptual Framework
Public Cloud
Platforms
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

33. The Commons:
Business Model
[George Komatsoulis]

34. 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

35. Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Collaboration
• Training

36. Elements of The Digital Enterprise
Communities Policies
Infrastructure
• Intersection:
• Sustainability
• Efficiency
• Collaboration
• Training

37. Workforce Training

38. Problem: Lack of
Biomedical Data Science Specialists
BD2K T32/T15
BD2K K01
Career path workshops – eg AAU
Challenge
model of
funding

39. 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

40. I not only use all the brains
I have, but all I can borrow.
– Woodrow Wilson

41. 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
rogrammatic Theme
Deliverable
Example Features • IC’s
• Researchers
• Federal
Agencies
• International
Partners
• Computer
Scientists
Scientific Data Council External Advisory Board
Training

42. NIHNIH……
Turning Discovery Into HealthTurning Discovery Into Health
philip.bourne@nih.gov