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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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.
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.
This presentation covers the agenda of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
This presentation covers the final presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
This presentation shares a 10 minute pitch of big data potentials in the field of life sciences as presented at the 2015 CMS Global Life Science Forum on Nov 9, 2015 in Frankfurt
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This presentation covers the "Analyze Genomes: A Federated In-Memory Computing Platform for Life Sciences" 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 shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
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.
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.
This presentation covers the agenda of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
This presentation covers the final presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
This presentation shares a 10 minute pitch of big data potentials in the field of life sciences as presented at the 2015 CMS Global Life Science Forum on Nov 9, 2015 in Frankfurt
Festival of Genomics 2016 London: Analyze Genomes: A Federated In-Memory Comp...Matthieu Schapranow
This presentation covers the "Analyze Genomes: A Federated In-Memory Computing Platform for Life Sciences" presentation of the 2016 Festival of Genomics workshop "Big Medical Data in Precision Medicine: Challenges or Opportunities?" on Jan 19, 2016 in London.
The Driver of the Healthcare System in the 21st Century: Real-world Applicati...Matthieu Schapranow
The document discusses the driver of the healthcare system in the 21st century. It describes how patients, clinicians, and researchers interact and how their interactions will change. It also discusses the challenges of distributed and heterogeneous healthcare data sources, and proposes approaches like in-memory databases and real-time analysis of big medical data to address these challenges. Specific examples discussed include analyzing genomes and creating a medical knowledge cockpit to link patient specifics with international healthcare knowledge.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
Analyze Genomes: A Federated In-Memory Database System For Life SciencesMatthieu Schapranow
1) Dr. Matthieu-P. Schapranow presented on Analyze Genomes, a federated in-memory database system for life sciences.
2) The system aims to provide real-time analysis of big medical data while maintaining sensitive data locally due to privacy and locality restrictions.
3) It incorporates local compute resources by installing worker nodes to process sensitive data locally and store results in local database instances, while being managed as part of a larger federated database system.
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.
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.
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.
The document discusses challenges and opportunities presented by big medical data and describes an approach using in-memory technology. It proposes a medical knowledge cockpit that allows interactive exploration of distributed medical data sources. This would facilitate tasks like identifying relevant information for a patient's genes, finding suitable clinical trials, and interactively analyzing drug response data. The goal is to enable personalized medicine through real-time analysis of medical data from various sources.
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.
The document proposes a federated in-memory database system for life sciences that addresses the needs of patients, clinicians, and researchers by enabling real-time analysis of big medical data while maintaining data privacy and locality. It describes key actors and a use case in cancer treatment. The proposed solution incorporates local compute resources through a federated in-memory database with a cloud service provider managing shared algorithms and master data, while sensitive patient data resides locally.
Festival of Genomics 2016 London: Analyze Genomes: Modeling and Executing Gen...Matthieu Schapranow
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.
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.
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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.
Gesundheit geht uns alle an: Smart Data ermöglicht passendere Entscheidungen...Matthieu Schapranow
The document discusses how Analyze Genomes provides real-time analysis of big medical data to enable precision medicine. It analyzes diverse data sources, from genomes to clinical trials, using an in-memory database. This allows identifying best treatment options, such as finding no-small cell lung cancer patients the most effective drug. Analyze Genomes also powers related digital health applications and research projects that integrate data from various healthcare partners.
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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.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
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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 document summarizes Dr. Matthieu-P. Schapranow's presentation at the Festival of Genomics in Boston on turning big medical data into precision medicine. It describes an in-memory database approach that enables real-time analysis of heterogeneous medical data sources. This allows clinicians and researchers to interactively explore patient data, clinical trials, pathways, and literature to obtain personalized treatment recommendations. The system was designed using a human-centered methodology to ensure usability, effectiveness, and feasibility for precision medicine applications.
Analyze Genomes: A Federated In-Memory Database System For Life SciencesMatthieu Schapranow
1) Dr. Matthieu-P. Schapranow presented on Analyze Genomes, a federated in-memory database system for life sciences.
2) The system aims to provide real-time analysis of big medical data while maintaining sensitive data locally due to privacy and locality restrictions.
3) It incorporates local compute resources by installing worker nodes to process sensitive data locally and store results in local database instances, while being managed as part of a larger federated database system.
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.
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.
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.
The document discusses challenges and opportunities presented by big medical data and describes an approach using in-memory technology. It proposes a medical knowledge cockpit that allows interactive exploration of distributed medical data sources. This would facilitate tasks like identifying relevant information for a patient's genes, finding suitable clinical trials, and interactively analyzing drug response data. The goal is to enable personalized medicine through real-time analysis of medical data from various sources.
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.
The document proposes a federated in-memory database system for life sciences that addresses the needs of patients, clinicians, and researchers by enabling real-time analysis of big medical data while maintaining data privacy and locality. It describes key actors and a use case in cancer treatment. The proposed solution incorporates local compute resources through a federated in-memory database with a cloud service provider managing shared algorithms and master data, while sensitive patient data resides locally.
Festival of Genomics 2016 London: Analyze Genomes: Modeling and Executing Gen...Matthieu Schapranow
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.
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.
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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.
Gesundheit geht uns alle an: Smart Data ermöglicht passendere Entscheidungen...Matthieu Schapranow
The document discusses how Analyze Genomes provides real-time analysis of big medical data to enable precision medicine. It analyzes diverse data sources, from genomes to clinical trials, using an in-memory database. This allows identifying best treatment options, such as finding no-small cell lung cancer patients the most effective drug. Analyze Genomes also powers related digital health applications and research projects that integrate data from various healthcare partners.
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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 showcases examples of how artificial intelligence technology can be used to improve the patient journey in the specific medical field of oncology.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
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.
Algorithmen statt Ärzte: Algorithmen statt Ärzte: Ersetzt Big Data künftig ...Matthieu Schapranow
The document discusses whether algorithms will replace doctors in medicine. It notes that healthcare costs are rising significantly. While algorithms and health apps promise benefits like improved prevention, quality issues exist if not regulated as medical products. The document explores various use cases where algorithms already augment doctors, such as automatically segmenting tissues in scans. Citizens increasingly demand digital health services and control over their own data. The conclusion is that algorithms and doctors can work together, with algorithms handling routine tasks and doctors focusing on personal care, if challenges around regulation and data protection are addressed.
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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 document summarizes Dr. Matthieu-P. Schapranow's presentation at the Festival of Genomics in Boston on turning big medical data into precision medicine. It describes an in-memory database approach that enables real-time analysis of heterogeneous medical data sources. This allows clinicians and researchers to interactively explore patient data, clinical trials, pathways, and literature to obtain personalized treatment recommendations. The system was designed using a human-centered methodology to ensure usability, effectiveness, and feasibility for precision medicine applications.
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.
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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.
Enabling Real-time Genome Data Research with In-memory Database Technology (S...Matthieu Schapranow
Dr. Schapranow presented on using in-memory database technology to enable real-time genome data research. Key points include:
- Current genome analysis takes 4-6 weeks but in-memory technology can accelerate it to minutes or less.
- The High-Performance In-Memory Genome project aims to analyze a patient's full genomic and medical record data during a doctor's visit.
- Tests on SAP HANA showed genome analyses like variant calling and allele counting were 82-600x faster than traditional methods.
- The in-memory approach enables interactive exploration of patient cohorts and pathways to better understand treatment effectiveness.
1) In-memory applications are revolutionizing oncology research by enabling fast access and analysis of large amounts of individual patient data, clinical trials data, research findings, and more.
2) Researchers are developing tools that incorporate all available individual patient information, reference latest lab results and medical knowledge, and allow interactive analysis to help clinicians personalize cancer treatment in real time.
3) Key challenges include analyzing and combining distributed heterogeneous medical data sources rapidly. Technologies using in-memory databases aim to address this by enabling analysis of large datasets in seconds.
Enabling Real-Time Genome Data Research with In-Memory Database Technology (I...Matthieu Schapranow
This document discusses enabling real-time genome data research using in-memory database technology. It describes how in-memory databases can perform genomic analyses like variant calling and clustering of patient cohorts much faster than traditional disk-based approaches. The document outlines several research topics like improving data preparation pipelines and integrating genetic pathways. It also presents results showing how an in-memory database loaded a large genome dataset and was able to perform queries 82-600 times faster than conventional tools. The future potential for combining genomic and clinical data in real-time to help researchers, clinicians and patients is also discussed.
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The document discusses challenges of big data processing for personalized medicine. It describes the vision of using large amounts of diverse medical data like genomes, medical records, clinical trials, and research papers to enable personalized preventative care and more effective therapies for patients. The speaker then outlines their approach using in-memory databases and analytics to enable interactive analysis of this data. Examples discussed include tools for researchers to analyze genomes, clinicians to find comparable patient cases, and patients to identify relevant clinical trials.
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Globalization of Clinical Trials: Mutual acceptance of Medical Device dataAnnet Visscher
Technologies and regulatory standards facilitate clinical trial globalization and mutual acceptance of clinical trial data. Changes in trial execution, however, are not 1:1 reflected in foreign data acceptance. Factors such as ethnic and local requirements seem to outweigh the benefits.
Open human genome data - presentation at the annual TKT/CLIDP doctoral programme symposium 2016 "Open up! – Open Data and Open Access" of the University of Turku.
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Festival of Genomics 2016 London: Analyze Genomes: Real-world Examples
1. Analyze Genomes: Real-world Examples
Dr. Matthieu-P. Schapranow
Festival of Genomics, London, U.K.
Jan 19, 2016
2. What are the Trends?
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
2
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
3. 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
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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6. Schapranow, Festival of
Genomics, Jan 19, 2016
Recap: we.analyzegenomes.com
Real-time Analysis of Big Medical Data
6
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:
Real-world Examples
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources
7. 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, Festival of
Genomics, Jan 19, 2016
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Real-world Examples
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8. Cloud-based Services for Processing of DNA Data
■ 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:
Real-world Examples
Standardized Modeling and
runtime environment for
analysis pipelines
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Schapranow, Festival of
Genomics, Jan 19, 2016
9. ■ 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
Medical Knowledge Cockpit for Patients and Clinicians
Linking Patient Specifics with International Knowledge
Analyze Genomes:
Real-world Examples
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Schapranow, Festival of
Genomics, Jan 19, 2016
10. Medical Knowledge Cockpit for Patients and Clinicians
■ 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!
Analyze Genomes:
Real-world Examples
Automatic clinical trial
matching build on text
analysis features
Unified access to structured
and un-structured data
sources
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Genomics, Jan 19, 2016
11. Schapranow, Festival of
Genomics, Jan 19, 2016
Medical Knowledge Cockpit for Patients and Clinicians
Pathway Topology 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 Analyze Genomes:
Real-world Examples
Unified access to multiple formerly
disjoint data sources
Pathway analysis of genetic
variants with graph engine
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12. Schapranow, Festival of
Genomics, Jan 19, 2016
■ Interactively explore relevant publications, e.g. PDFs
■ Improved ease of exploration, e.g. by highlighted medical terms and relevant
concepts
Medical Knowledge Cockpit for Patients and Clinicians
Publications
Analyze Genomes:
Real-world Examples
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13. ■ In-place preview of relevant data, such as publications and publication meta data
■ Incorporating individual filter settings, e.g. additional search terms
Medical Knowledge Cockpit for Patients and Clinicians
Publications
Analyze Genomes:
Real-world Examples
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Schapranow, Festival of
Genomics, Jan 19, 2016
14. Schapranow, Festival of
Genomics, Jan 19, 2016■ Personalized clinical trials, e.g. by incorporating patient specifics
■ Classification of internal/external trials based on treating institute
Medical Knowledge Cockpit for Patients and Clinicians
Latest Clinical Trials
Analyze Genomes:
Real-world Examples
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15. Real-time Data Analysis and
Interactive Exploration
Drug Response Analysis
Data Sources
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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cetuximab might be more
beneficial for the current case
19. Real-time Processing of Event Data from Medical Sensors
■ Processing of sensor data, e.g. from Intensive Care
Units (ICUs) or wearable sensor devices (quantify self)
■ Multi-modal real-time analysis to detect indicators for
severe events, such as heart attacks or strokes
■ Incorporates machine-learning algorithms to detect
severe events and to
inform clinical
personnel in time
■ Successfully tested
with 100 Hz event
rate, i.e. sufficient
for ICU use
Analyze Genomes:
Real-world Examples
Comparison of waveform data
with history of similar patients
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Genomics, Jan 19, 2016
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20. Real-time Assessment of Clinical Trial Candidates
■ Switch from trial-centric to patient-centric clinical trials
■ Real-time matching and clustering of patients and
clinical trial inclusion/exclusion criteria
■ No manual pre-screening of patients for months:
In-memory technology enables interactive pre-
screening process
■ Reassessment of already screened or already
participating patient reduces recruitment costs
Analyze Genomes:
Real-world Examples
Assessment of patients
preconditions for clinical trials
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Genomics, Jan 19, 2016
21. Drug Safety
Statistical Analysis of Drug Side Effects Data
■ Combines confirmed side effect data from different
data sources
■ Interactive statistical analysis, e.g. apriori rules, to
discover still unknown interactions
■ Integrates personal prescription data and directly
report side effects
■ Work together with your doctor to prevent interaction
with already prescribed drugs
Analyze Genomes:
Real-world Examples
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Schapranow, Festival of
Genomics, Jan 19, 2016
Unified access to
international side effect data
On-the-fly extension of
database schema to add side
effect databases
+++
22. Schapranow, Festival of
Genomics, Jan 19, 2016
From University to Market
Oncolyzer
■ Research initiative for exchanging relevant
tumor data to improve personalized treatment
■ 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 clinicians,
clinical researchers, and software engineers
■ Honored with the 2012 Innovation Award of the
German Capitol Region
Analyze Genomes:
Real-world Examples
Unified access to formerly disjoint
oncological data sources
Flexible analysis on patient’s
longitudinal data
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t
23. ■ Combines patient’s
longitudinal time series data
with individual analysis
results
■ Real-time analysis across
hospital-wide data using
always latest data when
details screen is accessed
■ http://analyzegenomes.com/
apps/oncolyzer-mobile-app/
From University to Market
Oncolyzer: Patient Details Screen
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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24. ■ Allows real-time analysis on
complete patient cohort
■ Supports identification of
clinical trial participants
based on their individual
anamnesis
■ Flexible filters and various
chart types allow graphical
exploration of data on
mobile devices
From University to Market
Oncolyzer: Patient Analysis Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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25. ■ Shows all patients the logged-
in clinician is assigned for
■ Provides overview about most
recent results and treatments
for each patient
■ http://global.sap.com/
germany/solutions/
technology/enterprise-
mobility/healthcare-apps/
mobile-patient-record-app.epx
From University to Market
SAP EMR: Patient Overview Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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26. ■ Displays time series data, e.g.
temperature or BMI
■ Allows graphical exploration of
time series data
From University to Market
SAP EMR: Patient Detail Screen
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Genomics, Jan 19, 2016
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Real-world Examples
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27. ■ Flexible combination of
medical data
■ Enables interactive and
graphical exploration
■ Easy to use even without
specific IT background
From University to Market
SAP Medical Research Insights
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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28. Master’s Project “Global Medical Knowledge”
Winter Semester 2015/2016
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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Markus
The Team mpws2015hp@hpi.de
■ Lars Rückert
■ Friedrich Horschig
■ Benjamin Reißaus
■ Markus Dücker
Supervisors
■ Milena Kraus
■ Dr. Matthieu-P. Schapranow
■ Dr. Matthias Uflacker
29. ■ Motivation:
□ Combine individual patient-specific, heart-associated data
□ Support real-time data analysis
□ Support discovery of predictive markers
■ Contribution
□ Collect data from multiple sources
□ Integrate data into single in-memory database system
□ Support graphical data analysis
Motivation and Contribution
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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30. Challenges
Distributed Heterogeneous Data Sources in Life Sciences
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Genomics, Jan 19, 2016
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Real-world Examples
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■ Data resides in different physical locations
■ Data is stored in heterogeneous data formats
31. Our Approach
Integrated Data Analysis Platform
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Genomics, Jan 19, 2016
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Real-world Examples
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32. Rapid Prototype
Web Application with Real Trial Data
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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33. Rapid Prototype
Graphical Data Exploration
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Genomics, Jan 19, 2016
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Real-world Examples
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34. ■ Create web app for individual user roles
□ Interview all domain experts involved in data acquisition process
□ Extend web application to individual needs
■ Extend analysis capabilities
□ Graphical data exploration
□ User-specific visualization
Outlook & Next Steps
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Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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35. ■ Hasso Plattner Institute
■ Analyze Genomes Platform and Application Examples
■ Methodology & Technology
■ Current Student Projects
■ Discussion and Q&A
Agenda
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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36. ■ Global Medical Knowledge (Master’s project)
■ Markers for cardiovascular diseases to
assess treatment options (DHZB)
■ Combine health data to improve health care
research (AOK)
■ Pharmacogenetics (Bayer)
■ Generously supported by
Join us for upcoming projects!
Schapranow, Festival of
Genomics, Jan 19, 2016
Analyze Genomes:
Real-world Examples
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Interdisciplinary
Design Thinking
Teams
You?
37. ■ 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
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Genomics, Jan 19, 2016
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Analyze Genomes:
Real-world Examples
38. 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/
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Genomics, Jan 19, 2016
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Real-world Examples
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