4th International Conference on Big Data and Information Analytics, Theories, Algorithms and Applications in Data Science, December 17-19, 2018, Houston Texas. https://sph.uth.edu/divisions/biostatistics/bigdia/

1. Big Data & Analytics Across the
Interdisciplinary Divide
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
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@pebourne

2. Perspective
• I was not trained as a data scientist or computer scientist - I
started as a physical chemist
• At this point I can’t give you a deep technical perspective
• My examples are taken from biomedicine, but broadly
applicable
• Deeply engaged in preparing one academic institution for a very
different data driven interdisciplinary future
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3. My motivation
The biggest gains for our society are going to come
through interdisciplinary research where data and
analytics catalyze the collaboration
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4. Consider a wake up call of sorts
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5. A wake up call of sorts
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https://www.sciencemag.org/news/2018/12/google-s-deepmind-aces-protein-folding
https://moalquraishi.wordpress.com/2018/12/09/alphafold-casp13-what-just-happened/

6. Data as driver
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https://www.ebi.ac.uk/uniprot/TrEMBLstats
Contents of the Protein Data Bank

7. This is a somewhat predictable outcome..
The real excitement comes from the unexpected …
Witness the tale of the trauma surgeon …
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But there is more…

8. Air pollution-ecosystem feedback: unmanned
aerial vehicles and ecosystem models to
quantify ozone-forest interactions
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• Spatial heterogeneity
• Novel sampling
• Senor data
Departments:
Environmental Sciences
Electrical Engineering

9. A working definition of what we are doing …
It is the unexpected re-use of information which is
the value added by the web
Tim Berners-Lee
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https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#116a5a2d55cf

10. A working definition of what we are doing …
It is the unexpected re-use of information which is
the value added by the web and subsequent
analysis of that information for societal benefit
Tim Berners-Lee / Phil Bourne
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https://www.forbes.com/sites/gilpress/2013/05/28/a-very-short-history-of-data-science/#116a5a2d55cf

11. Of course this was all predicted by smart people ..
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12. 12
https://en.wikipedia.org/wiki/Jim_Gray_(computer_scientist)
https://www.microsoft.com/en-us/research/wp-
content/uploads/2009/10/Fourth_Paradigm.pdf
https://twitter.com/aip_publishing/status/856825353645559808
12/17/18 BigDIA

13. I would suggest that this audience has a
responsibility to promote the fourth paradigm
which is not a well recognized phenomenon across
disciplines …
Here is one example of how to do so
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14. How Will Science Change?
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15. 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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16. Model
Transportability
Horizontal
Integration
Multi-scale
Integration
human
mouse
zebrafish
DNA
Gene/Protein
Network
Cell
Tissue
Organ
Body
Population
CNV SNP methylation
3D structure Gene
expression Proteomics
Metabolomics
MetabolicSignaling
transduction
Gene
regulation
Hepatic Myoepithelial Erythrocyte
Epithelial Muscle Nervous
Liver Kidney Pancreas Heart
Physiologically based
pharmacokinetics
GWASPopulation
dynamics
Microbiota
Open, complex, diverse digital data
Systems Pharmacology
Xie et al. Annu Rev Pharmacol Toxicol. 2017 57:245-262
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16
BigDIA

17. How should we think about organizing ourselves in
an interdisciplinary way to maximize the
opportunities offered by the fourth paradigm?
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18. The Pillars of Data Science
18
Application Domains
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19. Lets briefly focus on those five pillars
in the context of one area of
biomedical informatics – structural
bioinformatics
What kinds of interchange should be
taking place between this field and
data science?
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Mura et al. 2018 Curr Opin Struct Biol. 52:95-102

20. Data Acquisition
• Persistence of raw data not clear
• Some level of consistency across instrument manufacturers
• Lessons in community/society drive
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Mura et al. 2018 Curr Opin Struct Biol. 52:95-102

21. Data Integration and Engineering
• URI’s no - stooped in tradition
• Ontologies – somewhat
• Linked data - somewhat
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Years of experience to convey

22. Data Analytics
22
–SVM’s
–Random forest
–Neural nets
–Deep learning
–??
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Opportunity to learn from many domains

23. Visualization & Dissemination
• Avoid the curse of the
ribbon
• Think sonics
• Look to video games
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24. Ethics, Law & Policy –
Community Driven Data Sharing
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25. How to implement this at any level?
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26. Guiding Principles
• Be constantly strategic and nimble - think supply chain
• Be sustainable - do not over reach
• Be interdisciplinary
• Be a organization without walls
• Be diverse, accessible and open
• Be team not individually driven
• Strive for quality not quantity in education & research
• Be innovative and translational through new forms of engagement with
the private sector, government, NGOs, local, state, national and
international partners
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27. Guiding Principles
• Be constantly strategic and nimble - think supply chain
• Be sustainable - do not over reach
• Be interdisciplinary
• Be a organization without walls
• Be diverse, accessible and open
• Be team not individually driven
• Strive for quality not quantity in education & research
• Be innovative and translational through new forms of engagement with
the private sector, government, NGOs, local, state, national and
international partners
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28. Be Interdisciplinary – Be Without Walls
• Satellites – discipline driven - located in another School
focusing on the mission of that School where data and
analytics play a role, e.g.,
– SOM – data governance and clinical translation
– Education – working on educational analytics
• Centers – Focus area driven e.g.
– Ethics and justice
– Neurodegenerative disorders – Alzheimer's, autism, TBI
– Sports analytics
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29. Guiding Principles
• Be constantly strategic and nimble - think supply chain
• Be sustainable - do not over reach
• Be interdisciplinary
• Be a organization without walls
• Be diverse, accessible and open
• Be team not individually driven
• Strive for quality not quantity in education & research
• Be innovative and translational through new forms of engagement with
the private sector, government, NGOs, local, state, national and
international partners
2912/17/18 BigDIA

30. Be Diverse, Accessible and Open – Why?
• Data science exists largely because of open data
• Open knowledge encourages disciplinary and interdisciplinary
collaboration
• Yet much of the scholarship we produce is not accessible at all and
certainly not accessible to socioeconomically disadvantaged groups
• Gouging by commercial knowledge providers is making the
knowledge produced by others less accessible to us
• Research is suffering from a reproducibility crisis addressable
through greater access to all aspects of the research lifecycle
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31. Be Diverse, Accessible and Open – Why?
Consider Biomedicine
• Big Data
– Total data from NIH-funded research back 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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32. 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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33. 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 Resnick12/17/18 BigDIA 33

34. 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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35. 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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36. 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

37. 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

38. 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

39. 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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40. 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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41. The NIH through the Big Data to Knowledge
(BD2K) is experimenting with a platform,
keeping in mind the need to overcome these
impediments
Enter The Commons
https://en.wikipedia.org/wiki/Ealing_Common
#/media/File:Ealing_Common_-
_geograph.org.uk_-_17075.jpg12/17/18 BigDIA 41

42. 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
Commons –
Initial focus is on integrating two
layers of the scholarly workflow
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43. Commons topology
Compute Platform: Cloud or HPC
Services: APIs, Containers, Indexing,
Software: Services & Tools
scientific analysis tools/workflows
Data
“Reference” Data Sets
User defined data
DigitalObjectCompliance
App store/User Interface
PaaS
SaaS
IaaS
https://datascience.nih.gov/commons
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44. Commons Compliance
• Treat products of research – data,
methods, papers etc. as digital objects
• These digital objects exist in a shared
virtual space
• Digital object compliance through FAIR
principles:
– Findable
– Accessible (and usable)
– Interoperable
– Reusable
https://commonfund.nih.gov/bd2k/commons
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45. 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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46. Impediments to platforms
• 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/
10-barriers-to-employee-
innovation/#8bdbaa811133
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47. Even if they are successful, platforms are likely to be
domain specific and only address the
infrastructure..
What else is needed?
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48. We need to promote 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
• Support global community efforts….
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49. Wikidata – fast growing
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• Get on board with developments in schema.org, knowledge
graphs, etc… as part of the rule rather than the exception
• Provide metadata and opinion for data we produce or use

50. Let me summarize:
How do we address the interdisciplinary divide?
• Promote the fourth paradigm
• Work within your institutions to promote data science as an
interdisciplinary field
• Establish an open and integrated environment for data and
analytics
• Be patient and do not oversell …
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51. 12/17/18 BigDIA 51
Haas & Schmidt 2018
http://iswc2018.semanticweb.org/workshops-tutorials/#ekg

52. Acknowledgements
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The BD2K Team at NIH
The 150 folks who have passed through my laboratory
https://docs.google.com/spreadsheets/d/1QZ48UaKcwDl_iFCvBmJsT03FK-bMchdfuIHe9Oxc-rw/edit#gid=0

53. Thank You
peb6a@virginia.edu
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