What are today's challenges of big medical data and how can we use the immense data to turn it into potentials, e.g. for precision medicine. Get insights in application examples, where big medical data are incorporated and how in-memory database technology can enable it instantaneous analysis.
Processing of Big Medical Data in Personalized Medicine: Challenge or PotentialMatthieu Schapranow
Experience our AnalyzeGenomes.com services at the example of the Medical Knowledge Cockpit and how it can improve the daily work for researchers and physicians.
The given slide deck was presented on the 2017 Festival of Genomics in London, UK. It depicts how latest in-memory database technology supports clinicians in finding the best treatment options incorporating genetic data.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital HealthMatthieu Schapranow
The slide deck of the presentation "AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital Health" of the 2017 BMBF All Hands Meeting in Karlsruhe are online available now.
Processing of Big Medical Data in Personalized Medicine: Challenge or PotentialMatthieu Schapranow
Experience our AnalyzeGenomes.com services at the example of the Medical Knowledge Cockpit and how it can improve the daily work for researchers and physicians.
The given slide deck was presented on the 2017 Festival of Genomics in London, UK. It depicts how latest in-memory database technology supports clinicians in finding the best treatment options incorporating genetic data.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital HealthMatthieu Schapranow
The slide deck of the presentation "AnalyzeGenomes.com: A Federated In-Memory Database Platform for Digital Health" of the 2017 BMBF All Hands Meeting in Karlsruhe are online available now.
Patient Journey in Oncology 2025: Molecular Tumour Boards in PracticeMatthieu Schapranow
The slide deck was presented at the Bio Data World Congress in Basel on Dec. 04, 2019. It shares first results from the work in the HiGHmed consortium on the use case oncology.
The given presentation was presented at SAPPHIRE 2017 in Orlando, FL on May 18, 2017. It highlights latest research results focusing on user-centered in-memory applications for precision medicine.
KConnect - making Medical Information Easier to Find: Semantic Annotation and...Peter Voisey
Outline of the work Findwise is doing in the KConnect EU innovation project and how linked data is used. It includes information about the KConnect technologies (including semantic annotation and semantic search) that make medical information easier to find - whatever it's type e.g. EHRs/EMRs, medical literature, guidelines etc.
The given presentation showcases examples of how artificial intelligence technology can be used to improve the patient journey in the specific medical field of oncology.
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
Abstract:In health care domain, data mining plays a vital role for predicting diseases. For detecting a disease number of tests should be required from the patient but number of test should be reduced while using data mining techniques. The data mining technique analyze the test parameters and it concludes the associative relation between the parameters that reduces the tests and the reduced test plays key role in time and performance. In this study medical terms such as sex, blood pressure, and cholesterol like nineteen input attributes are used. In this paper association among various attributes which are the causative factors of heart diseases are analyzed. The patient’s records are observed before prediction and the factors are grouped as per its severity level. In this system the level of causative factors are categorized using K-Means clustering technique and it distinguishes the risky and non-risky factors. Frequent risk factors are mined from the clinical heart database using Apriori algorithm. The risk factors are taken for this study to predict the risk level and find the co-ordination among the factors that helps the medical people to predict the disease with minimum tests and treatments.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
This is the presentation as shown on the 2015 Future Convention in Frankfurt, Germany on Nov 23, 2015. It shows latest research results in the field of precision medicine using the Drug Response Analysis app of the http://we.analyzegenomes.com platform.
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
Festival of Genomics 2016 London: Real-time Exploration of the Cancer Genome,...Matthieu Schapranow
This presentation covers the NCT presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
Sharing and standards christopher hart - clinical innovation and partnering...Christopher Hart
Acknowledging the increasing need for cooperation and collaboration in data sharing and access. Describing the complexity that this can bring. Then describing some of the ways to simplify that.
Originally presented at Terrapin's Clinical innovation and partnering world March 8-9 2017.
http://www.terrapinn.com/conference/innovation-and-partnering/index.stm
How Big Data is Transforming Medical Information Insights - DIA 2014CREATION
Daniel Ghinn's presentation at DIA 8th Annual European Medical Information and Communications Conference explores the use of big data in medical information...
Patient Journey in Oncology 2025: Molecular Tumour Boards in PracticeMatthieu Schapranow
The slide deck was presented at the Bio Data World Congress in Basel on Dec. 04, 2019. It shares first results from the work in the HiGHmed consortium on the use case oncology.
The given presentation was presented at SAPPHIRE 2017 in Orlando, FL on May 18, 2017. It highlights latest research results focusing on user-centered in-memory applications for precision medicine.
KConnect - making Medical Information Easier to Find: Semantic Annotation and...Peter Voisey
Outline of the work Findwise is doing in the KConnect EU innovation project and how linked data is used. It includes information about the KConnect technologies (including semantic annotation and semantic search) that make medical information easier to find - whatever it's type e.g. EHRs/EMRs, medical literature, guidelines etc.
The given presentation showcases examples of how artificial intelligence technology can be used to improve the patient journey in the specific medical field of oncology.
Presentation Alliance of European Life Sciences Law Firms
(Julian Hitchcock and Sofie van der Meulen) on legal aspects of big data in pharma. Topics: privacy, IP, medical devices and IVD.
Abstract:In health care domain, data mining plays a vital role for predicting diseases. For detecting a disease number of tests should be required from the patient but number of test should be reduced while using data mining techniques. The data mining technique analyze the test parameters and it concludes the associative relation between the parameters that reduces the tests and the reduced test plays key role in time and performance. In this study medical terms such as sex, blood pressure, and cholesterol like nineteen input attributes are used. In this paper association among various attributes which are the causative factors of heart diseases are analyzed. The patient’s records are observed before prediction and the factors are grouped as per its severity level. In this system the level of causative factors are categorized using K-Means clustering technique and it distinguishes the risky and non-risky factors. Frequent risk factors are mined from the clinical heart database using Apriori algorithm. The risk factors are taken for this study to predict the risk level and find the co-ordination among the factors that helps the medical people to predict the disease with minimum tests and treatments.
Big Data and its Impact on Industry (Example of the Pharmaceutical Industry)Hellmuth Broda
While we bemoan the ever increasing data tsunami new technologies allow to harvest the gold nuggets in the hay stack.
Using the example of the Pharmaceutical Industry some of the possible business uses for Big Data Analitics are outlined.
This is the presentation as shown on the 2015 Future Convention in Frankfurt, Germany on Nov 23, 2015. It shows latest research results in the field of precision medicine using the Drug Response Analysis app of the http://we.analyzegenomes.com platform.
Dr. Dennis Wang discusses possible ways to enable ML methods to be more powerful for discovery and to reduce ambiguity within translational medicine, allowing data-informed decision-making to deliver the next generation of diagnostics and therapeutics to patients quicker, at lowered costs, and at scale.
The talk by Dr. Dennis Wang was followed by a panel discussion with Mr. Albert Wang, M. Eng., Head, IT Business Partner, Translational Research & Technologies, Bristol-Myers Squibb.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
Festival of Genomics 2016 London: Real-time Exploration of the Cancer Genome,...Matthieu Schapranow
This presentation covers the NCT presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
Sharing and standards christopher hart - clinical innovation and partnering...Christopher Hart
Acknowledging the increasing need for cooperation and collaboration in data sharing and access. Describing the complexity that this can bring. Then describing some of the ways to simplify that.
Originally presented at Terrapin's Clinical innovation and partnering world March 8-9 2017.
http://www.terrapinn.com/conference/innovation-and-partnering/index.stm
How Big Data is Transforming Medical Information Insights - DIA 2014CREATION
Daniel Ghinn's presentation at DIA 8th Annual European Medical Information and Communications Conference explores the use of big data in medical information...
This presentation, by big data guru Bernard Marr, outlines in simple terms what Big Data is and how it is used today. It covers the 5 V's of Big Data as well as a number of high value use cases.
Slides of the 2015 Bio Data World Congress show how our analyzegenomes.com services are combined to support precision medicine in the context of modern oncology treatment.
Hasso Plattner gave this presentation about how in-memory technology can support analysis of big medical data at the 2013 World Health Summit in Berlin. It consists real-world examples showing latest results of partners, such as the Hasso Plattner Institute, Stanford, Charité, and SAP. For background details, please refer to http://we.analyzegenomes.com
Festival of Genomics 2016 London: Challenges of Big Medical Data?Matthieu Schapranow
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.
The given presentation outlines services of the cloud platform "Analyze Genomes" enabling precision medicine. It was presented on the mHealth meets Diagnostics symposium in Berlin on Jun 21, 2016.
A Federated In-Memory Database Computing Platform Enabling Real-Time Analysis...Matthieu Schapranow
The slide deck "A Federated In-Memory Database Computing Platform Enabling Real-time Analysis of Big Medical Data" presented on May 17, 2017 at Intel Tech Talks hosted by SAPPHIRE 2017 in Orlando, FL is online available now.
This presentation provides a brief overview of how in-memory database technology can be applied to support systems medicine approaches. For that, it shares real-world experiences, e.g. from the SMART project consortium funded by the German Federal Ministry of Education and Research.
A Tool for Optimizing De-Identified Health Data for Use in Statistical Classi...arx-deidentifier
Presented at IEEE CBMS 2017: When individual-level health data is shared in biomedical research the privacy of patients and probands must be protected. This is typically achieved with methods of data de-identification, which transform data in such a way that formal guarantees about the degree of protection from re-identification can be provided. In the process it is important to minimize loss of information to ensure that the resulting data is useful. A typical use case is the creation of predictive models for knowledge discovery and decision support, e.g. to infer diagnoses or to predict outcomes of therapies. A variety of methods have been developed which can be used to build robust statistical classifiers from de-identified data. However, they have not been tuned for practical use and they have not been implemented into mature software tools. To bridge this gap, we have extended ARX, an open source anonymization tool for health data, with several new features. We have implemented a method for optimizing the suitability of de-identified data for building statistical classifiers and a method for assessing the performance of classifiers built from de-identified data. All methods are accessible via a comprehensive graphical user interface. We have used our methods to create logistic regression models from a patient discharge dataset for predicting the costs of hospital stays. The results show that our method enables the creation of privacy-preserving classifiers with optimal prediction accuracy.
Festival of Genomics 2016 London: Analyze Genomes: Real-world ExamplesMatthieu Schapranow
This presentation covers the "Analyze Genomes: Real-world Examples" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
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ICT Platform to Enable Consortium Work for Systems Medicine of Heart FailureMatthieu Schapranow
The slide deck "ICT Platform to Enable Consortium Work for Systems Medicine of Heart Failure" was presented on Oct 5, 2016 at the 2016 e:Med Meeting on Systems Medicine in Kiel, Germany.
Festival of Genomics 2016 London: Mining and Processing of Unstructured Medic...Matthieu Schapranow
This presentation covers the "Mining and Processing of Unstructured 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.
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This presentation covers the "Analyze Genomes: Modeling and Executing Genome Data Processing Pipelines" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
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LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
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Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
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Big Medical Data – Challenge or Potential?
1. Big Medical Data – Challenge or Potential?
Personalized Medicine Conference, Berlin, March 7th, 2014
Dr. Matthieu-P. Schapranow, Hasso Plattner Institute
2. Hasso Plattner Institute
Key Facts
■ 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, 150 PhD students
■ Course of study: IT Systems Engineering
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 20142
3. Hasso Plattner Institute
Programs
■ Full university curriculum
■ Bachelor (6 semesters)
■ Master (4 semesters)
■ Orthogonal Activities:
□ E-Health Consortium
□ School of Design Thinking
□ Research School
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 20143
4. Hasso Plattner Institute
Enterprise Platform and Integration Concepts Group
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
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 20144
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
5. Our Motivation
Personalized Medicine
■ Motivation: Can we analyze the entire data of a patient, incl. Electronic
Health Records (EHR) and genome data, during a doctor’s visit?
■ Genome data analysis may add up to weeks,
i.e. biopsy, biological preparation, sequencing,
alignment, variant calling, full analysis, and evaluation
■ Issue: Complex and time-consuming data processing tasks
■ In-memory technology accelerates genome data processing
□ Highly parallel alignment / variant calling
□ Real-time analysis of individual patient or cohort data
□ Combined search in structured / unstructured data
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 20145
6. Our Challenge
Distributed Big Data Sources
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 20146
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 data
Cancer patient records
>160k records at NCT
7. 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 Approach
In-Memory Technology
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 20147
+
++
+
+
P
v
+++
t
SQL
x
x
T
disk
9. Our Vision
Personalized Medicine
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Desirability
■ Leveraging directed customer services
■ Portfolio of integrated services for clinicians, researchers, and
patients
■ Include latest research results, e.g. most effective therapies
Viability
■ Enable personalized medicine also in far-off
regions and developing countries
■ Share data via the Internet to get feedback
from word-wide experts (cost-saving)
■ Combine research data (publications,
annotations, genome data) from international
databases in a single knowledge base
Feasibility
■ HiSeq 2500 enables high-coverage whole
genome sequencing in ≈1d
■ IMDB enables allele frequency
determination of 12B records within <1s
■ 1 relevant out of 80M annotations <1s
■ Data preparation as a service reduces TCO
9
10. High-Performance In-Memory Genome Project
Integration of Genomic Data
■ Preprocessing of DNA (alignment,
variant calling) can be modeled and is
executed as integrated process
■ Results are directly stored in in-memory
databases, e.g. for
□ Statistical analyses, and
□ Links to latest research knowledge
■ Real-time analysis of genome data
enables completely new way of research
and therapies, e.g. instant comparison
with patient cohorts
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 201410
11. Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 201411
High-Performance In-Memory Genome Project
Architectural Overview
Real-Time Capturing and Data Analysis
In-Memory Database
All Relevant Medical Information
*omics
Electronic
Medical Records
Service
Line Items
Patients Doctors Insurers Researchers
Information and Feedback within the Window of Opportunity
...
12. High-Performance In-Memory Genome Project
Selected Research Topics
Improving Analyses:
■ Information combination, e.g. Medical Knowledge Cockpit, Oncolyzer
■ Genome Browser enables deep dive into the genome
■ Cohort Analysis, e.g. clustering of patient cohorts
■ Combined Search, e.g. in clinical trials and side-effect databases
■ Pathways Topology Analysis, e.g. to identify cause/effect
Improving Data Preparations:
■ Graphical modeling of Genome Data Processing (GDP) pipelines
■ Scheduling and execution of multiple GPD pipelines in parallel
■ App store for medical knowledge (bring algorithms to data)
■ Exchange of sensitive data, e.g. history-based access control
■ Billing processes for intellectual property and services
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 201412
13. High-Performance In-Memory Genome Project
HANA Oncolyzer
■ Research initiative for exchanging relevant
tumor data to improve personalized treatment
■ Honored 2012 by the Innovation Award of
the German Capitol Region
■ In-memory technology as key-enabler for
real-time analysis of tumor data in
seconds instead of hours
■ Information available at your fingertips:
In-memory technology on mobile devices,
e.g. iPad
■ Interdisciplinary cooperation between
□ Medical doctors,
□ Researchers, and
□ Software engineers.
13 Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
14. High-Performance In-Memory Genome Project
HANA Oncolyzer
Patient Details Screen
■ Combines patient’s time
series data of specific
patient and analysis results
of patient cohort
■ Real-time analysis across
hospital-wide data
whenever details screen is
accessed
■ http://epic.hpi.uni-
potsdam.de/Home/
HanaOncolyzer
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 201414
15. High-Performance In-Memory Genome Project
HANA Oncolyzer
Patient Analysis Screen
■ Allows to real-time analysis
on complete patient cohort
■ Flexible filters and various
chart types allow graphical
exploration of data on
mobile devices
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 201415
16. High-Performance In-Memory Genome Project
Medical Knowledge Cockpit
■ Search for affected genes in distributed and
heterogeneous data sources
■ Immediate exploration of relevant information,
such as
□ Gene descriptions,
□ Molecular impact and related pathways,
□ Scientific publications, and
□ Suitable clinical trials.
■ No manual searching for hours or days:
In-memory technology translates searching into
interactive finding!
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Automatic clinical trial
matching build on text
analysis features
Unified access to
structured and un-
structured data sources
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17. High-Performance In-Memory Genome Project
Search in Structured and Unstructured Medical Data
■ Extended text analysis feature by medical
terminology
□ Genes (122,975 + 186,771 synonyms)
□ Medical terms and categories (98,886 diseases
+ 48,561 synonyms, 47 categories)
□ Pharmaceutical ingredients (7,099 + 5,561
synonyms)
■ Indexed clinicaltrials.gov database (145k trials/
30,138 recruiting)
■ Extracted, e.g., 320k genes, 161k ingredients,
30k periods
■ Select all studies based on multiple filters in less
than 500ms
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Clinical trial matching using
text analysis features
Unified access to structured
and unstructured data sources
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T
18. High-Performance In-Memory Genome Project
Analysis of Patient Cohorts
■ In a patient cohort, a subset does not respond to
therapy – why?
■ Clustering using various statistical algorithms,
such as k-means or hierarchical clustering
■ Calculation of all locus combinations in which at
least 5% of all TCGA participants have
mutations: 200ms for top 20 combinations
■ Individual clusters are calculated in parallel
directly within the database
■ K-means algorithm: 50ms (PAL) vs. 500ms (R)
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Fast clustering directly performed
within the in-memory database
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19. High-Performance In-Memory Genome Project
Genome Browser
■ Genome Browser: Comparison of multiple
genomes with reference
■ Combined knowledge base: latest relevant
annotations and literature, e.g. NCBI, dbSNP, and
UCSC
■ Detailed exploration of genome locations and
existing associations
■ Ranked variants, e.g. accordingly to known
diseases
■ Links to more detailed sources enable fast
identification of relevant information while
eliminating long-lasting searches.
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Unified access to multiple
formerly disjoint data sources
Matching of genetic variants
and relevant annotations
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20. High-Performance In-Memory Genome Project
Pathway Analysis
■ Search in pathways is limited to “is a certain
element contained” today
■ Integrated >1,5k pathways from international
sources, e.g. KEGG, HumanCyc, and
WikiPathways, into HANA
■ Implemented graph-based topology exploration
and ranking based on patient specifics
■ Enables interactive identification of possible
dysfunctions affecting the course of a therapy
before its start
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Unified access to multiple
formerly disjoint data sources
Pathway analysis of genetic
variants with graph engine
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21. High-Performance In-Memory Genome Project
Drug Response Testing
■ Drug response depends on individual genetic
variants of tumors
■ Challenge: Identification of relevant genetic
variants and their impact on drug response is a
ongoing research activity, e.g. Xenograft models
■ Exploration of experiment results is time-
consuming and Excel-driven
■ In-memory technology enables interactive
exploration of experiment data to leverage new
scientific insights
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Interactive analysis of
correlations between drugs
and genetic variants
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22. What to take home?
Test it: http://we.AnalyzeGenomes.com
For researchers
■ Enable real-time analysis of medical data
■ Automatic assessment of data, e.g. scan of pathways to identify
cellular impact of mutations
■ Combined free-text search in publications, diagnosis, and EMR
data, i.e. structured and unstructured data
For clinicians
■ Preventive diagnostics to identify risk patients early
■ Indicate pharmacokinetic correlations
■ Scan for similar patient cases, e.g. to evaluate therapy success
For patients
■ Identify relevant clinical trials and medical experts
■ Start most appropriate therapy as early as possible
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 201422
23. Keep in contact with us!
Big Medical Data: Challenge or Potential? Personalized Medicine Conference, Dr. Schapranow, Mar 7, 2014
Hasso Plattner Institute
Enterprise Platform & Integration Concepts
Dr. Matthieu-P. Schapranow
August-Bebel-Str. 88
14482 Potsdam, Germany
Dr. Matthieu-P. Schapranow
schapranow@hpi.uni-potsdam.de
http://we.analyzegenomes.com/
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