This presentation covers the "Challenges of Big Medical Data" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.

1. Challenges of Big Medical Data
Dr. Matthieu-P. Schapranow
Festival of Genomics, London, U.K.
Jan 19, 2016

2. ■ Online: Visit we.analyzegenomes.com for latest research results, tools, and news
■ Offline: Read more about it, e.g. High-Performance In-Memory Genome Data Analysis:
How In-Memory Database Technology Accelerates Personalized Medicine, In-Memory
Data Management Research, Springer, ISBN: 978-3-319-03034-0, 2014
■ In Person: Join us for “Cebit” March 14-20, 2016 in Hanover, Germany.
Important things first:
Where do you find additional information?
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
2

3. What is the Hasso Plattner Institute, Potsdam, Germany?
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
3

4. ■ Founded as a public-private partnership
in 1998 in Potsdam near Berlin, Germany
■ Institute belongs to the
University of Potsdam
■ Ranked 1st in CHE since 2009
■ 500 B.Sc. and M.Sc. students
■ 10 professors/chairs, 150 PhD students
■ Course of study: IT Systems Engineering
Hasso Plattner Institute
Key Facts
Challenges of Big
Medical Data
4
Schapranow, Festival of
Genomics, Jan 19, 2016

5. Prof. Dr. h.c. Hasso Plattner
■ Research focuses on the technical aspects of enterprise
software and design of complex applications
□ In-Memory Data Management for Enterprise
Applications
□ Enterprise Application Programming Model
□ Scientific Data Management
□ Human-Centered Software Design and Engineering
■ Industry cooperations, e.g. SAP, Siemens, Audi, and EADS
■ Research cooperations, e.g. Stanford, MIT, and Berkeley
Hasso Plattner Institute
Enterprise Platform and Integration Concepts Group
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
5
Partner of Stanford
Center for Design
Research
Partner of MIT in
Supply Chain
Innovation and
CSAIL
Partner at
UC Berkeley
RAD / AMP Lab
Partner of SAP
AG

6. ■ Since 2009 Program Manager E-Health & Life Sciences
■ 2006-2014 Strategic Projects SAP HANA
■ Visiting Scientist at V.A., Boston, MA and Charité, Berlin
■ Software Engineer by training (PhD, M.Sc., B.Sc.)
With whom are you dealing?
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
6

7. Healthcare Interactions in the 21st Century
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
7
Indirect Interaction
Direct Interaction
C linician PatientResearcher
Pharm aceutical
Com pany
H ealthcare
Providers
H ospital
Research
Center
Laboratory
Patient
Advocacy
G roup

8. ■ Patients
□ Individual anamnesis, family history, and background
□ Require fast access to individualized therapy
■ Clinicians
□ Identify root and extent of disease using laboratory tests
□ Evaluate therapy alternatives, adapt existing therapy
■ Researchers
□ Conduct laboratory work, e.g. analyze patient samples
□ Create new research findings and come-up with treatment alternatives
The Setting
Actors in Oncology
Schapranow, Festival of
Genomics, Jan 19, 2016
8
Challenges of Big
Medical Data

9. ■ Motivation: Can we enable patients to:
□ Understand and monitor their diseases to document the impact on their lives,
□ Receive latest information about their (chronic) diseases,
□ Cooperatively exchange with physicians and patients to improve quality of living
Our Motivation
Make Precision Medicine Become Routine
Challenges of Big
Medical Data
9
Schapranow, Festival of
Genomics, Jan 19, 2016

10. Our Methodology
Design Thinking
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
10

11. Our Methodology
Design Thinking
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
11
Desirability
■ Portfolio of integrated services for clinicians, researchers, and patients
■ Include latest treatment option, e.g. most effective therapies
Viability
■ Enable precision medicine also in far-off
regions and developing countries
■ Involve word-wide experts (cost-saving)
■ Combine latest international data
(publications, annotations, genome data)
Feasibility
■ HiSeq X Ten delivers 30x coverage
whole genome of >18k humans / year
■ IMDB enables allele frequency
determination of 12B records within <1s
■ Cloud-based data processing services
reduce TCO

12. Sequencing vs. Main Memory Costs
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
12
0.001
0.01
0.1
1
10
100
1000
10000
Jan2002
Jan2004
Jan2006
Jan2008
Jan2010
Jan2012
Jan2014
Costsin[USD]
Date
Main Memory Costs per Megabyte
Sequencing Costs per Megabase

13. IT Challenges
Distributed Heterogeneous Data Sources
13
Human genome/biological data
600GB per full genome
15PB+ in databases of leading institutes
Prescription data
1.5B records from 10,000 doctors and
10M Patients (100 GB)
Clinical trials
Currently more than 30k
recruiting on ClinicalTrials.gov
Human proteome
160M data points (2.4GB) per sample
>3TB raw proteome data in ProteomicsDB
PubMed database
>23M articles
Hospital information systems
Often more than 50GB
Medical sensor data
Scan of a single organ in 1s
creates 10GB of raw dataCancer patient records
>160k records at NCT
Challenges of Big
Medical Data
Schapranow, Festival of
Genomics, Jan 19, 2016

14. ■ Requirements
□ Real-time data analysis
□ Maintained software
■ Restrictions
□ Data privacy
□ Data locality
□ Volume of “big medical data”
■ Solution?
□ Federated In-Memory Database System vs. Cloud Computing
Software Requirements in Life Sciences
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
14

15. Clouds Trends?
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
15
Gartner's 2014 Hype Cycle for Emerging Technologies

16. Schapranow, Festival of
Genomics, Jan 19, 2016
we.analyzegenomes.com
Real-time Analysis of Big Medical Data
16
In-Memory Database
Extensions for Life Sciences
Data Exchange,
App Store
Access Control,
Data Protection
Fair Use
Statistical
Tools
Real-time
Analysis
App-spanning
User Profiles
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
Challenges of Big
Medical Data
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

17. Keep in contact with us!
Hasso Plattner Institute
Enterprise Platform & Integration Concepts (EPIC)
Program Manager E-Health
Dr. Matthieu-P. Schapranow
August-Bebel-Str. 88
14482 Potsdam, Germany
Dr. Matthieu-P. Schapranow
schapranow@hpi.de
http://we.analyzegenomes.com/
Schapranow, Festival of
Genomics, Jan 19, 2016
Challenges of Big
Medical Data
17