big data in life sciences

Big Data in Life Sciences

Dr. Matthieu-P. Schapranow CMS Global Life Sciences Forum, Frankfurt, Germany

Nov 9, 2015

What is the Hasso Plattner Institute, Potsdam, Germany?

Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015


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What are the Trends?



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https://www.google.com/trends/explore#q=Big data%2C Life sciences%2C Precision medicine&cmpt=q @ Nov 9, 2015

Life Sciences Big Data Precision Medicine

IT Challenges in Life Sciences Distributed Heterogeneous Data Sources

Human genome/biological data >750GB per complete human genome >15PB in databases of leading institutes

Prescription data 1.5B records from 10,000 doctors and 10M Patients (100 GB)

Clinical trials >30k recruiting trials on ClinicalTrials.gov

Human proteome 160M data points (2.4GB) per sample >3TB raw proteome data in ProteomicsDB

PubMed database >24M unstructured data in publications

Hospital information systems >50GB structured relational data

Medical sensor data Scan of a single organ creates 10GB of raw data within 1s

Cancer patient records >160k records only at NCT


Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Chart 4

Healthcare Interactions in the 21st Century



5 Ind irect In te raction

D irect In te raction

C lin ic ian P atien tR esearcher

P harm aceu tica lC om pany

H ea lthcareP roviders

H osp ita lR esearch

C enterLabora to ry

P atien tA dvocacy

G roup

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

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, CMS Global Life Sciences, Fankfurt, Nov 9, 2015


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we.analyzegenomes.com Real-time Analysis of Big Medical Data

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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


Drug Response Analysis

Pathway Topology Analysis

Medical Knowledge Cockpit Oncolyzer

Clinical Trial Recruitment

Cohort Analysis

...

Indexed Sources

Real-time Data Analysis and Interactive Exploration

Drug Response Analysis Data Sources



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



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Showcase



12 Calculating Drug Response… Predict Drug Response



13 cetuximab might be more

beneficial for the current case

■  Online: Visit we.analyzegenomes.com for latest research results, slides, videos, tools, and publications

■  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 “Festival of Genomics” Jan 19-21, 2016 in London, UK

Where do you find additional information?



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Keep in contact with us!

Hasso Plattner Institute Enterprise Platform & Integration Concepts (EPIC)

Program Manager E-Health August-Bebel-Str. 88

14482 Potsdam, Germany

Dr. Matthieu-P. Schapranow [email protected] http://we.analyzegenomes.com/



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big data in life sciences

Health & Medicine