American Institute of Medical and Biological Engineeting (AIMBE) presentation to the Academic Council. Washington DC, April 8, 2018

1. Big Data and Data Science:
Opportunities for Biomedical
Engineering
Philip E. Bourne PhD, FACMI
Stephenson Chair of Data Science
Director, Data Science Institute
Professor of Biomedical Engineering
peb6a@virginia.edu
https://www.slideshare.net/pebourne
04/08/18 AIMBE Academic Council 1
@pebourne

2. Disclaimer
• This is mostly NOT a talk about my own
research
• It draws upon my now one-year old view of
NIH as the former Associate Director for Data
Science (ADDS)
• It suffers from my drinking my own Kool-aid at
the University of Virginia
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3. Take home (hopefully)
• Increased awareness of the value of data
science to your activities
• Increased awareness of where NIH is headed
• Some thoughts about how to build out data
science in your own institutions
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4. Big data and data science are like the
Internet…
If I asked you to define them you would all
say something different, yet you use them
every day…
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http://vadlo.com/cartoons.php?id=357

5. So what do I mean by big data/data
science?
• Use of the ever increasing amount of open, complex,
diverse digital data
• Finding ways to ask and then answer relevant
questions by combining such diverse data sets
• Arriving at statistically significant conclusions not
otherwise obtainable
• Sharing such findings in a useful way
• Translating such findings into actions that improve
the human condition
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6. Cause
• There are ~2.7 Zetabytes (2.7 x 106 PB) of digital data
• Volume is doubling every two years
• Sheer volume of digital data e.g., $1000 genome,
wearable sensors, mandatory EHRs
• New tools e.g., Deep Artificial Neural Networks (DNNs)
• New computing power e.g., GPUs
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7. Effect
• Big data currently estimated as a $50bn
business – could save $3.1tn
• 50% growth in data/yr; 5% growth in IT
expenditure
• US 140,000- 190,000 unfilled deep data
analytics jobs
• UVA DSI has 600 applicants this year for 50
spots; MSDS/MBA highly sought
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8. Effect ++
• Big data currently estimated as a $50bn business
– could save $3.1tn – private sector research
• 50% growth in data/yr; 5% growth in IT
expenditure - undervalued
• US 140,000- 190,000 unfilled deep data analytics
jobs – competition for skilled researchers high
• DSI has 600 applicants this year for 50 spots;
MSDS/MBA highly sought – large human capital
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9. How much biomedical data?
• Big Data
– Total data from NIH-funded research in 2016
estimated at 650 PB*
– 20 PB of that is in NCBI/NLM (3%) and it is
expected to grow by 10 PB in 2016
• Dark Data
– Only 12% of data described in published papers is
in recognized archives – 88% is dark data^
• Cost
– 2007-2014: NIH spent ~$1.2Bn extramurally on
maintaining data archives
* In 2012 Library of Congress was 3 PB
^ http://www.ncbi.nlm.nih.gov/pubmed/26207759
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10. Consider some current high profile NIH
examples where and how data science is
being applied
• Moonshot - platforms and
integration, ML
• MODs – automated curation
• Human Microbiome Project –
new cloud based tools, ML
• TOPMed - platforms and
integration
• All-of-Us - platforms and
integration
• ECHO – platforms and
integration
• BRAIN - ML
10
All: Analytics, the Commons, FAIR, sustainability, workforce
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11. What of the future?
One view is the 6D’s
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12. Digitization
Deception
Disruption
Demonetization
Dematerialization
Democratization
Time
Volume,Velocity,Variety
Digital camera invented by
Kodak but shelved
Megapixels & quality improve slowly;
Kodak slow to react
Film market collapses;
Kodak goes bankrupt
Phones replace
cameras
Instagram,
Flickr become the
value proposition
Digital media becomes bona fide
form of communication
From a presentation to the Advisory Board to the NIH Director
Example - photography
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13. A call for making these data open
• Mandates
– NIH, NSF, Data
Management Plans
• Business models can be
protected yet everyone
benefits
• It saves lives ….
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14. Why a More Open Process?
Use case:
Diffuse Intrinsic Pontine Gliomas (DIPG)
• Occur 1:100,000
individuals
• Peak incidence 6-8 years
of age
• Median survival 9-12
months
• Surgery is not an option
• Chemotherapy ineffective
and radiotherapy only
transitive
From Adam Resnick04/08/18 AIMBE Academic Council 14

15. Timeline of genomic studies in DIPG
• Landmark studies identify
histone mutations as
recurrent driver mutations in
DIPG ~2012
• Almost 3 years later, in
largely the same datasets,
but partially expanded, the
same two groups and 2
others identify ACVR1
mutations as a secondary, co-
occurring mutation
From Adam Resnick
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16. What do we need to do differently to
reveal ACVR1?
• ACVR1 is a targetable kinase
• Inhibition of ACVR1 inhibited tumor
progression in vitro
• ~300 DIPG patients a year
• ~60 are predicted to have ACVR1
• If large scale data sets were only
integrated with TCGA and/or rare
disease data in 2012, ACVR1 mutations
would have been identified
• 60 patients/year X 3 years = 180
children’s lives (who likely succumbed to
the disease during that time) could have
been impacted if only data were FAIR
From Adam Resnick
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17. How to promote
departmental/institutional openness?
• Encourage persistent identifiers e.g., ORCID
• Encourage preprints
• Encourage Open Access (OA)
• Recognize openness in hiring and P&T
• Teach open scholarship
• Promote institutional openness – repositories,
wikimedian in residence
• Support institutional open data governance
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18. NIH Strategic Plan for Data
• Support a Highly Efficient and
Effective Biomedical Research
Data Infrastructure
• Promote Modernization of the
Data-Resources Ecosystem
• Support the Development and
Dissemination of Advanced
Data Management, Analytics,
and Visualization Tools
• Enhance Workforce
Development for Biomedical
Data Science
• Enact Appropriate Policies to
Promote Stewardship and
Sustainability
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https://grants.nih.gov/grants/rfi/NIH-Strategic-Plan-for-Data-Science.pdf

19. Research Data Infrastructure …
Both funders and some institutions
see the need to move from pipes to
platforms to accelerate research…
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https://blog.lexicata.com/wp-content/uploads/2015/03/platform-model-
750x410.png

20. If platforms are the answer we could
ask the question…
Will biomedical research become more
like Airbnb?
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Vivien Bonazzi
Should biomedical research be Like Airbnb?
doi: 10.1371/journal.pbio.2001818

21. I am not crazy, hear me out
• Airbnb is a platform that supports a trusted relationship
between consumer (renter) and supplier (host)
• The platform focuses on maximizing the exchange of services
between supplier and consumer and maximizing the amount
of trust associated with a given stakeholder
• It seems to be working:
– 60 million users searching 2 million listings in 192 countries
– Average of 500,000 stays per night.
– Evaluation of US $25bn
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Should biomedical research be Like Airbnb?
doi: 10.1371/journal.pbio.2001818

22. Platforms will ultimately digitally
integrate the scholarly workflow for
human and machine analysis
Should biomedical research be Like Airbnb?
doi: 10.1371/journal.pbio.2001818
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23. Paper Author Paper Reader
Data Provider Data Consumer
Employer Employee
Reagent Provider Reagent Consumer
Software Provider Software Consumer
Grant Writer Grant Reviewer
Supplier Consumer Platform
MS Project
Google Drive
Coursera
Researchgate
Academia.edu
Open Science
Framework
Synapse
F1000
Rio
Educator Student
Pilot Open Data Lab
(ODL) underway
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24. Why a comparison to Airbnb is not fair
• Airbnb was born digital
• The exchange of services on Airbnb are
simple compared to what is required of a
platform to support biomedical research
Nevertheless there is much to be
learnt
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25. Impediments to a biomedical platform
• Current work practices by all stakeholders
• Entrenched business models
• Size of the undertaking aka resources
needed
• Trust
• Incentives to use the platform
http://www.forbes.com/sites/johnhall/2013/04/29/1
0-barriers-to-employee-innovation/#8bdbaa811133
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26. Such platforms combined with
emerging analytics will likely have
significant impact on biomedical
engineering
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27. Machine learning has been around for
over 20 years – why now?
• Amount of data available for training
• Open source - R and python
• Advances in computing (e.g., GPU’s) allow for deeper
neural nets (deep learning)
• Algorithmic efficiency gains (e.g., in back
propagation)
• Success promotes further research
• Commercialization
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Pastur-Romay et al. 2016 doi:10.3390/ijms17081313

28. Let me touch on our research in
protein engineering oh so briefly….
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Structural Biology Meets Data Science – Does Anything
Change?
Crowd Source: Current Opinions in Structural Biology 2018
https://docs.google.com/document/d/1rD3Qh1btTYlnGkKefN
GSFVq8v_mqRNa8I0o5MP3ZMW4/edit

29. Are their new scaffolds out there Nature
has yet to discover that AI could?
There are ~ 20300 possible proteins
>>>> all the atoms in the Universe
96M protein sequences from
73,000 species (source RefSeq)
135,000 protein structures
yield 1221 folds (SCOPe 2.06)
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30. AIMBE Academic Council 30
At DeepMind, which is based in London,
AlphaGo Zero is working out how proteins
fold, a massive scientific challenge that
could give drug discovery a sorely needed
shot in the arm.
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31. 04/08/18 AIMBE Academic Council 31
http://cartertoons.com/

32. How should academic institutions
think about exploiting data science?
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33. Organization: core data science
verticals
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Data Integration
& Engineering
Machine Learning
& Analytics
Visualization
& Dissemination
Data Acquisition Ethics, Law,
Policy,
Social Implications
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34. Organization: interdisciplinary
horizontals
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Data Integration
& Engineering
Machine Learning
& Analytics
Visualization
Data Acquisition
& Dissemination
Ethics, Law,
Policy,
Social Implications
Biomedical Engineering
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35. Data Acquisition
• Sensors
• Nanotechnology
• Imaging
• Unexpected sources e.g., DMV
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36. Data Integration and
Engineering
• Ontologies
• Object identifiers
• Indexing schemes
• Common data models
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37. Biomedical:
Machine Learning &
Analytics
• Neural nets
• Deep learning
• Natural Language
Processing (NLP)
• Gene expression &
neurological disease (Kipnis)
• Predicting opioid overdose
(VA Health)
• Predicting escalating care
and mortality risk of
cirrhosis patients (UVA HS)
• Human microbiome &
mental health in maternal
health (Psychology &
Nursing)
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38. Biomedical:
Visualization
• Virtual Reality (VR)
• Networks
• Sonics
• Visualizing microbial
stability (Biology &
Systems)
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39. Ethics, Law,
Policy & Social
Implications
• Data sharing
• Privacy
• Normativity
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Wendy Novicoff, Ph.D
04/08/18

40. Conclusion:
Driven by large amounts of open
digital data of different types and new
algorithms and approaches biomedical
researchers are destined to follow the
private sector towards the fourth
paradigm
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41. Acknowledgements
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The BD2K Team at NIH
My Colleagues at UVA
The 150 folks who have passed through my laboratory
https://docs.google.com/spreadsheets/d/1QZ48UaKcwDl_iFCvBmJsT03FK-bMchdfuIHe9Oxc-rw/edit#gid=0

42. Thank You
peb6a@virginia.edu
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