This is the slide deck presented at the IHK/Bitkom Big Data Summit in Berlin on Sep 27, 2016 .

1. Gesundheit geht uns alle an:
Smart Data ermöglicht passendere Entscheidungen – in Echtzeit!
Dr. Matthieu-P. Schapranow
Bitkom/IHK Big Data Summit Berlin
Sep 27, 2016

2. Patient Doctor Interaction
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
2
What about the
relevant data?

3. IT Challenges of Healthcare
Distributed Heterogeneous Data Sources
3
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
Analyze Genomes
Services for Precision
Medicine
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016

4. Combined column
and row store
Map/Reduce Single and
multi-tenancy
Lightweight
compression
Insert only
for time travel
Real-time
replication
Working on
integers
SQL interface on
columns and rows
Active/passive
data store
Minimal
projections
Group key Reduction of
software layers
Dynamic multi-
threading
Bulk load
of data
Object-
relational
mapping
Text retrieval
and extraction engine
No aggregate
tables
Data partitioning Any attribute
as index
No disk
On-the-fly
extensibility
Analytics on
historical data
Multi-core/
parallelization
Our Technology
In-Memory Database Technology
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
4
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine

5. Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Digital Health powered by AnalyzeGenomes.com:
Real-time Analysis of Big Medical Data
5
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
Analyze Genomes
Services for Precision
Medicine
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources

6. Use Case: Precision Medicine in Oncology
Identification of Best Treatment Option for Cancer Patient
■ Patient: 48 years, female, non-smoker, smoke-free environment
■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV
■ Markers: KRAS, EGFR, BRAF, NRAS, (ERBB2)
1. Surgery to remove tumor
2. Tumor sample is sent to laboratory to extract DNA
3. DNA is sequenced resulting in 750 GB of raw data per sample
4. Processing of raw data to perform analysis
5. Identification of relevant driver mutations using international medical knowledge
6. Informed decision making
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
6

7. Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
7

8. Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
8

9. App Example: Integrating Processing and Real-time Analysis
of Genome Data in the Clinical Routine
■ Control center for processing of raw DNA data, such as
FASTQ, SAM, and VCF
■ Personal user profile guarantees privacy of uploaded
and processed data
■ Supports reproducible research process by storing all
relevant process parameters
■ Implements prioritized data processing and fair use, e.g.
per department or per institute
■ Supports additional service, such as data annotations,
billing, and sharing for all Analyze Genomes services
■ Honored by the 2014 European Life Science Award
Analyze Genomes
Services for Precision
Medicine
Standardized Modeling and
runtime environment for
analysis pipelines
9
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016

10. ■ Query-oriented search interface
■ Seamless integration of patient specifics, e.g. from EMR
■ Parallel search in international knowledge bases, e.g. for biomarkers, literature,
cellular pathway, and clinical trials
App Example:
Medical Knowledge Cockpit for Patients and Clinicians
Analyze Genomes
Services for Precision
Medicine
10
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016

11. Real-time Data Analysis and
Interactive Exploration
App Example: Identifying Best Chemotherapy using
Drug Response Analysis
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
Smoking status,
tumor classification
and age
(1MB - 100MB)
Raw DNA data
and genetic variants
(100MB - 1TB)
Medication efficiency
and wet lab results
(10MB - 1GB)
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Patient-specific
Data
Tumor-specific
Data
Compound
Interaction Data

12. Heart
Failure
Sleeping
disorder
Fibrosis
Blood
pressure
Blood
volume
Gene ex-
pression
Hyper-
trophyCalcium
meta-
bolism
Energy
meta-
bolism
Iron
deficiency
Vitamin-D
deficiency
Gender
Epi-
genetics
■ Integrated systems medicine based on
real-time analysis of healthcare data
■ Initial funding period: Mar ‘15 – Feb ‘18
■ Funded consortium partners:
Systems Medicine Model of Heart Failure (SMART)
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
12
A R
T
+
T
RAM
S
+
S
M

13. ■ Process definition through
user interviews
■ Identification of time-consuming and
manual process steps
■ Requirements for a computer-aided
research process:
1. Sharing of data
2. Improved communication
3. Reproducible data processing
Requirements Engineering for System Medicine
Computer-aided Systems Medicine Process
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
13

14. ■ In-Memory Database (IMDB) as
data integration platform
■ Automatic synchronization of
partner data
■ Event-driven notifications
■ Specific real-time analysis
workflows, e.g. for RNAseq
SMART Data Integration
and Analysis Platform
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
14

15. ■ Interdisciplinary partners collaborate on enabling real-time healthcare research
■ Initial funding period: Aug 2015 – July 2018
■ Funded consortium partners:
□ AOK
German healthcare insurance company
□ data experts group
Technology operations
□ Hasso Plattner Institute
Real-time data analysis, in-memory database technology
□ Technology, Methods, and Infrastructure for Networked Medical Research
Legal and data protection
Smart Analysis Health Research Access (SAHRA)
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
15

16. 1. AP4.12: Data exploration, e.g. graphical exploration and linkage of data
2. AP4.17: Matching, e.g. to identify sets of patients with similar features
3. AP4.22: Prediction, e.g. of future patient developments using training data from
similar patients
SAHRA
Pilot Applications
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
16

17. ■ Deep learning supporting radiologists at Charité, e.g. MRI segmentation
■ Natural language processing with DKG,
e.g. question answering, clinical trials, publications
■ Real-time Exploration of the Human Immune System, NCT Heidelberg
Further Digital Health Activities
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
17

18. Control your Personal Data
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
18
Data Donation Pass
Personal Interests
■ Asthma
■ Diabetes II
Notifications
■ 9/27: Granted Dr. House
access to resp. lab data
■ 9/25: Respiratory
laboratory data available
■ 9/21: Asthma self
management study publ.

19. ■ For patients
□ Identify relevant clinical trials and medical experts
□ Become an informed patient
■ For clinicians
□ Identify pharmacokinetic correlations
□ Scan for similar patient cases, e.g. to evaluate therapy efficiency
■ For researchers
□ Enable real-time analysis of medical data, e.g. assess pathways
to identify impact of detected variants
□ Combined mining in structured and unstructured data, e.g. publications,
diagnosis, and EMR data
What to Take Home?
Test it Yourself: AnalyzeGenomes.com
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
19
Analyze Genomes
Services for Precision
Medicine

20. Keep in contact with us!
Schapranow, Bitkom/IHK
Big Data Summit, Sep
27, 2016
Analyze Genomes
Services for Precision
Medicine
20
Dr. Matthieu-P. Schapranow
Program Manager E-Health & Life Sciences
Hasso Plattner Institute
August-Bebel-Str. 88
14482 Potsdam, Germany
schapranow@hpi.de
http://we.analyzegenomes.com/