Big Data Infrastructure for Translational Research discusses challenges in building big data infrastructure for translational research. It defines big data as large and complex data difficult to process with typical tools. Big data comes from various sources like mobile devices, sensors, clinical monitors. Scaling data acquisition from patient bed to institution is discussed. Tools used include databases, scripting languages, statistical packages and visualization. Challenges include data capture, curation, storage, sharing and analysis. A multidisciplinary team approach is advocated to tackle big data challenges in translational medicine.

1. Big Data Infrastructure for Translational
Research
Christopher G. Wilson, Ph.D.
Associate Professor Physiology and Pediatrics
Center for Perinatal Biology
Translational Medicine, April 18th, 2015

2. Disclosures
The work reported here was supported, in part,
by NIH grants:
1R01HL081622-01 (NHLBI)
1R03HD064830-01 (NICHD)

5. Outline
• Defining “Big Data”
• Big data is of multiple modes/types
• Scaling data acquisition to build Big Data sets
• Patient bed
• Unit
• Institution-wide
• Continuing challenges

7. What is “Big Data”?
• Big data is a blanket term for any collection of data sets so
large and complex that it becomes difficult to process using the
typical data management tools and data processing
applications.
• Big data usually includes data sets so large that commonly
used software (like Microsoft Office) cannot be used to
capture, curate, manage, and process the data quickly and
efficiently.
• Big data set sizes are a constantly moving target ranging
from 100’s of gigabytes (109 bytes), to terabytes (1012 bytes)
and even to petabytes (1015 bytes) of data in a single data set.

8. A feast of data!
• The world’s technological per-capita capacity to store
information has roughly doubled every 40 months since the
1980s
• Global Internet traffic has reached almost 1000 exabytes
(1018 bytes) annually and continues to grow*
• The challenge for both business and research science is
coming up with the tools to extract usable information from this
data
*Cisco systems estimate

9. Where does so much data come from?
Data sets grow to vast size because they are increasingly
being gathered by:
• Ubiquitous information-sensing mobile devices (phones,
fitbits, jawbones, etc.)
• Surveillance technologies (remote sensing devices like
drones or traffic cameras)
• Software logs from your internet activity (Hello—Facebook!)
• Radio-frequency identification (RFID) tags
• Wireless sensor networks (once again, the kind of thing your
phone “wants” to attach to when you are out and about)
• And scientific instruments, clinical monitors, patient
samples…

10. Personal
fitness
trackers

11. Work-flow of “Big Data” analysis

12. Or…
• Obtain data
• Scrub data
• Explore data
• Model the data
• Interpret the data
• Present the data

13. Data analytics is a team sport!
• Project manager—responsible for setting clear project objectives and deliverables.
The project manager should be someone with more experience in data analysis
and a more comprehensive background than the other team members.
• Statistician—should have a strong mathematics/statistics background and will be
responsible for reporting and developing the statistics workflow for the project.
• Visualization specialist—responsible for the design/development of data
visualization (figures/animation) for the project.
• Database specialist—develops ontology/meta-tags to represent the data and
incorporate this information in the team's chosen database schema.
• Content Expert—has the strongest background in the focus area of the project
(Physiologist, systems biologist, molecular biologist, biochemist, clinician, etc.) and
is responsible for providing background material relevant to the project's focus.
• Web developer/integrator—responsible for web-content related to the project,
including the final report formatting (for web/hardcopy display).
• Data analyst/programmer—the most junior member of the team will take on
general responsibilities to assist the other team members. This is a learning
opportunity for a team member who is new to data analysis and needs time to
develop the skills necessary to fully participate in the workflow.

14. Data analytics is a team sport!
Project manager/
content expert
(physician/scientist)
Database/web
developer
Statistician/
Data viz
Programmer
Team members can have multiple roles….

15. What tools are typically used?
• 64 bit computing environment is typical (Big RAM and Big
storage, massively parallel software running on clusters/cloud
servers)
• Data is acquired and stored in a database (SQL for some but
NOSQL databases like Hadoop, MongoDB, CouchDB,
Clusterpoint, etc. are “better”)
• Data screening & cleaning using “scripting” languages (Perl
or Python typically) and processing using tools like
MapReduce
• “Industrial strength” statistical packages (typically R, SAS, or
SPSS)
• Visualization (D3/IDL/MATLAB/Python/Plot.ly, etc.)
• Metadata tagging (XML and variants)

17. How can we meet the challenge
of Big Data collection/integration
in a translational setting?

18. What are the challenges for clinicians/researchers?
The amount of biomedical data that is increasingly available
provides both opportunity and challenge for the translational
investigator.
• Molecular biology has provided tools to allow understanding of
genomics and proteomics.
• There is growing data on the connectomics of signaling pathways
• Patient demographic data and other EHR/EMR metrics are a resource
that is only now being widely deployed and interrogated.
• Patient physiology (bedside monitors) can be used to provide
fundamental information about patient health and adaptation to
pathophysiologies.
• Health Insurance Portability and Accountability Act of 1996 (HIPAA) is
a necessary challenge for data handling.

19. Courtesy Michael De Georgia & J. Michael Schmidt

20. Big Data to Decisions!
» Technology challenges for “Data to Decisions”
~ Transforming data from multiple sources into meaningful information (evidence-context dependent)
~ Association of data from diverse heterogeneous, asynchronous sources
~ Merging/fusion of information for alerts and decision support
~ Human guided processing and analysis
Multi-source Analysis For Pattern Discovery Extract & synthesize
information from diverse
data.
SOURCE
SOURCE
SOURCE
Source-to-Evidence:
Information Processing &
Extraction
Text Analytics
Image Analysis
Signal Processing
Data Association
Data Fusion:
Alerting & Decision
Support
Combine
Information
Weigh
Evidence
Real time
Alerting
User Interface:
Display & Analysis
Visualization
Queries
Data
Provenance
Sensitivity

21. Real-time Decision Support
Providing useful information to the clinician
» Real-time decision support to clinicians at the point of care
~ Codify best practice protocols
~ Enable efficient treatment decisions
~ Reduce needless procedures
~ Optimize coordination among care givers
~ Reduce the probability of mistakes being made
» Key features that affect decision support
~ Methods to retrieve, merge, and present data and information
~ Algorithms to extract information from complex, heterogeneous data
~ Visualization/graphical feedback to better understand patient conditions
» Automated alerting for conditions of concern
~ Combining information across data streams
~ Accumulation of weak evidence from multiple sources
~ Enhanced retrieval and visualization of information

22. Challenges inherent in Big Data Analytics
• Capture
• Curation
• Storage
• Search
• Sharing
• Transfer
• Analysis
• Visualization

23. Data is multi-modal
Unified data set
Physiology
waveforms
(ECG, EEG,
SaO2, BP)
Radiology
(X-Ray, MRI, CAT,
etc.)
EMR/EHR
“-omics”
data

24. Bedside Patient Data Acquisition

25. Scaling to a hospital-wide data center
Ken Loparo
Michael DeGeorgia
Frank Jacono
Farhad Kaffashi

27. CWRU IMEDSTM Proof of Concept
Demonstration

28. Why is IMEDS™ Different?
The Approach
~ “Bottom-up” development with clinicians and engineers working
side-by-side
~ Open source architecture design
~ Total integrated, “plug-and-play” system solution
~ Unbiased approach
~ Unified effort, rather than stove-piped, “one-off” solutions to small
pieces of the problem
~ Non-profit nation-wide consortium
~ Builds on existing infrastructures
~ Leverages best available technology, regardless of source

29. Courtesy Michael De Georgia & J. Michael Schmidt

30. Challenges inherent in Big Data Analytics
• Capture
• Curation
• Storage
• Search
• Sharing
• Transfer
• Analysis
• Visualization

31. Courtesy of Susanna-Assunta Sansone, PhD

32. Courtesy of Susanna-Assunta Sansone, PhD

33. Courtesy of Susanna-Assunta Sansone, PhD

34. IPython
interface
http://ipython.org
• Reproducible
• Version controlled (git)
• Interactive analysis

35. Challenges inherent in Big Data Analytics
• Capture
• Curation
• Storage
• Search
• Sharing
• Transfer
• Analysis
• Visualization

36. Worldwide movement for FAIR data
Barend Mons and Susanna-Assunta Sansone
http://bd2k.nih.gov/workshops.html#ADDS

37. !
"
Launched on May 27th, 2014
A new online-only publication for descriptions of scientifically valuable datasets in
the life, environmental and biomedical sciences, but not limited to these
Credit for sharing
your data
Focused on reuse
and reproducibility
Peer reviewed,
curated
Promoting Community
Data Repositories
Open Access
Supported by:
Courtesy of Susanna-Assunta Sansone, PhD

38. Challenges inherent in Big Data Analytics
• Capture
• Curation
• Storage
• Search
• Sharing
• Transfer
• Analysis
• Visualization

39. Data Processing
Decision Tree
Analysis
Artificial Neural
Network
Mechanistic
Approaches
Graphical
Approaches
Bayesian
Network
Hierarchical
Clustering
Probabilistic
Approaches
Classical
Statistical
Inference
Bayesian
Statistical
Inference
Complex Systems Analysis
Time
Domain
Frequency
Domain
Scale Invariant
(Fractal) Analysis
Approximate
Entropy
Integrated
Patient
Database
Data Analysis Methods

40. Data Analysis Methods

41. Python as a data analytics environment

42. Advantages to using a Big Data approach
• Speed of data reduction and analysis
• Visualization of complex data sets can be done relatively
quickly
• Capacity for storage and processing of vast data sets is
inherent in the tool stack
• Scalability of cloud/cluster storage
• Potential for “Big Impact” on research and clinical care

43. Disadvantages to a Big Data approach
• Often not hypothesis driven (a fishing mission?)
• Requires expensive computing technology depending upon
data processing and storage needs
• Requires significant programming skill to develop and use the
tool stack
• Typically requires “team based” data analysis and
management (programmer, database manager, design/
visualization person, etc.)
• Just because you have lots of data, doesn’t mean you have
an obvious or easy way to extract the information!

44. Summary
• We live in a data-rich era.
• The data available to us is multi-modal and requires
integration.
• Data collection and integration can occur at many scales
(bedside to institution) but the data must be converted into
usable information.
• Team-based science depends upon a wide range of data
analytics skills.
• Curation, reproducibility of and shared access to data is an
ongoing challenge.

45. Where do you find your data
analytics team members?

46. Syllabus Overview (10 week course)
Foundations 1: Using text editors, using the IPython notebook for data exploration, using
version control software (git), using the class wiki.
Foundations 2: Using IPython/NumPy/SciPy, importing and manipulating data with Pandas,
data visualization in IPython.
Analysis Methods: Basic signal theory overview, time-series data, plotting (lines, histograms,
bars, etc.) dynamical systems analyses of data variability, information theory measures
(entropy) of complexity, frequency domain/spectral measures (FFT, time-varying spectrum),
wavelets.
Handling Sequence data: Using R/Bioconductor, differences between mRNA-Seq, gene-
array, proteomics, and deep-sequencing data, visualizing data from gene/RNA arrays.
Data set storage and retrieval: Basics of relational databases, SQL vs. NOSQL, cloud
storage/NAS/computing clusters, interfacing with Hadoop/MapReduce, metadata and ontology
for biomedical/patient data (XML), using secure databases (REDCap).
Data integrity and security: The Health Insurance Portability and Accountability Act (HIPAA)
and what it means for data management, de-identifying patient data (handling PHI), data
security best practices, making data available to the public—implications for data transparency
and large-scale data mining.

47. Coalition Institutions

48. The coding Queen and her Court…
Abby Dobyns
Princesses of Python
Rhaya Johnson
Regie Felix and Adaeze Anyanwu
And a Princeling….
Jamie Tillett

49. Acknowledgements
Loma Linda
• Andy Hopper
• Traci Marin
• Charles Wang
• Wilson Aruni
• Valery Filippov
CWRU
• Michael De Georgia
• Kenneth Loparo
• Frank Jacono
• Farhad Kaffashi
My laboratory’s git repository:
UC Riverside
• Thomas Girke
(Bioinformatics)
La Sierra University
• Marvin Payne
CSU San Bernardino
• Art Concepcion
(Bioinformatics)
UC Irvine
• Alex Nicolau
(Comp Sci/Bioinf)
https://github.com/drcgw/bass

50. Questions?!

51. Further reading
• Doing Data Science by Cathy O’Neil and Rachel Schutt
• Data Analysis with Open-Source Tools by Philipp Janert
• The Art of R Programming by Norman Matloff
• R for Everyone by Jared P. Lander
• Python for Data Analysis by Wes McKinney
• Think Python by Allen B. Downey
• Think Stats by Allen B. Downey
• Think Complexity by Allen B. Downey
• Every one of Edward Tufte’s books (The Visual Display
of Quantitative Information, Visual Explanations,
Envisioning Information, Beautiful Evidence)

52. Example: Patient physiology waveforms + EMR

53. Example: Interrogating sequence data