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.
A Federated In-Memory Database Computing Platform Enabling Real-Time Analysis...Matthieu Schapranow
1) Dr. Schapranow presents a federated in-memory database computing platform called AnalyzeGenomes.com to enable real-time analysis of big medical data.
2) The platform aims to incorporate all available patient data, reference latest lab results and medical knowledge, and support interactive analysis to help clinicians make treatment decisions.
3) It uses a distributed in-memory database across nodes to combine and link heterogeneous medical data sources while addressing challenges of data privacy, locality, and volume.
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.
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.
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 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.
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.
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.
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.
A Federated In-Memory Database Computing Platform Enabling Real-Time Analysis...Matthieu Schapranow
1) Dr. Schapranow presents a federated in-memory database computing platform called AnalyzeGenomes.com to enable real-time analysis of big medical data.
2) The platform aims to incorporate all available patient data, reference latest lab results and medical knowledge, and support interactive analysis to help clinicians make treatment decisions.
3) It uses a distributed in-memory database across nodes to combine and link heterogeneous medical data sources while addressing challenges of data privacy, locality, and volume.
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.
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.
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 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.
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.
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.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
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 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.
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.
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 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.
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.
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.
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 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 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
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.
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.
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.
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.
The given presentations share a specific use case from the medical field of oncology and outlines the potentials of applying artificial intelligence to it.
This presentation shows application examples of the analyzegenomes.com service for precision medicine. It was presented at 2016 HIMSS conference in Las Vegas, NV
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 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.
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.
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 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.
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.
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.
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 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 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
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.
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.
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.
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.
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.
The document discusses in-memory applications for informed patients using precision medicine. It describes the Hasso Plattner Institute which develops these applications using in-memory technology. The applications include real-time processing of medical sensor data, statistical analysis of drug side effects, and medical knowledge cockpits for patients and clinicians to access up-to-date research and clinical trials. The goal is to empower patients to better understand their health conditions and treatments.
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.
Introduction to High-performance In-memory Genome Project at HPI Matthieu Schapranow
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.
Gaining Time – Real-time Analysis of Big Medical Data SAP Technology
Growing volumes of diverse medical data from sources like genomes, proteomes, clinical records, medical sensors and clinical trials are creating new opportunities for innovation in medicine. SAP HANA is enabling real-time analysis of this big medical data through its ability to process large volumes of data in memory at rapid speeds. This allows for new scenarios like genome variant analysis across large populations in parallel, building proteomics-based cancer diagnostic pipelines interactively, and providing unified access to clinical data from different sources. Multidisciplinary teams combining clinical, research, technical and business expertise are needed to develop new collaborative approaches that are viable and can help drive improvements in areas like personalized healthcare and clinical decision making.
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
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.
Cooperation of HRB with Healthcare Providers and Communication with their Ope...WCIT 2014
This document discusses the cooperation between the Lower Saxony Bank of Health (LSBH) and healthcare providers in Lower Saxony, Germany. The LSBH was founded in 2011 based on an earlier eHealth project to establish a standard compliant solution for intersectoral communication in healthcare. The LSBH acts as a neutral organization to undertake legal/organizational tasks and provide central technical services to allow better networking between healthcare actors. The document outlines the organizational structure and basic principles of the LSBH, including that individual medical data remains stored at healthcare providers and two-stage patient consent is required for data sharing.
Demystifying Text Analytics and NLP in HealthcareHealth Catalyst
Leading the discussion, we have two exceptional thinkers in this space, Mike Dow, a former CIO and current Health Catalyst product manager and software developer, and Dr. Carolyn Simpkins, Health Catalyst’s Chief Medical Informatics Officer.
They will share thoughts on the challenges of text in clinical analytics as well as demonstrate:
Why text is an important part of clinical analytics
Why a text search is not enough
How clinical text search can be refined with NLP techniques
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.
This document discusses the use of electronic health records (EHRs) for clinical research purposes. It provides examples of how EHR data has been used by Kaiser Permanente to improve health outcomes and reduce mortality rates. It also outlines challenges in using EHR data, such as variations in disease treatment and outcomes across countries. The document proposes forming a learning health system using EHR data to continuously improve patient care and support clinical research. It describes projects that aim to address data quality, privacy, and enable the trusted sharing of EHR data for research.
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.
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.
This document provides an introduction to data science and analytics. It discusses why data science jobs are in high demand, what skills are needed for these roles, and common types of analytics including descriptive, predictive, and prescriptive. It also covers topics like machine learning, big data, structured vs unstructured data, and examples of companies that utilize data and analytics like Amazon and Facebook. The document is intended to explain key concepts in data science and why attending a talk on this topic would be beneficial.
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Overview
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2. What is the Hasso Plattner Institute, Potsdam, Germany?
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
2
3. ■ 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
■ 12 professors/chairs, 150 PhD students
■ Apr 2017: Digital Engineering Faculty
■ Oct 2017: Opening of Digital Health Center
Hasso Plattner Institute
Key Facts
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
3
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
4. ■ Can we enable clinicians to take their therapy decisions:
□ Incorporating all available patient specifics,
□ Referencing latest lab results and worldwide medical knowledge, and
□ In an interactive manner during their ward round?
Our Motivation
Turn Precision Medicine Into Clinical Routine
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
4
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
5. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
5
6. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
6
7. Our Vision
Medical Board Incorporating Latest Medical Knowledge
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
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8. The Challenge
Distributed Heterogeneous Data Sources
8
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: A
Federerated In-
Memory Database for
Digital Health
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
9. 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
9
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
10. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
10
In-Memory Database
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
11. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
11
In-Memory Database
Combined and Linked Data
Genome
Data
Cellular
Pathways
Genome
Metadata
Research
Publications
Pipeline and
Analysis Models
Drugs and
Interactions
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Indexed
Sources
12. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
12
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: A
Federerated In-
Memory Database for
Digital Health
Indexed
Sources
13. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Our Approach: AnalyzeGenomes.com
In-Memory Computing Platform for Big Medical Data
13
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: A
Federerated In-
Memory Database for
Digital Health
Drug Response
Analysis
Pathway Topology
Analysis
Medical
Knowledge CockpitOncolyzer
Clinical Trial
Recruitment
Cohort
Analysis
...
Indexed
Sources
14. Reproducibility
Modeling of Data Analysis Pipelines
1. Design time (researcher, process expert)
□ Definition of parameterized process model
□ Uses graphical editor and jobs from repository
2. Configuration time (researcher, lab assistant)
□ Select model and specify parameters, e.g. aln opts
□ Results in model instance stored in repository
3. Execution time (researcher)
□ Select model instance
□ Specify execution parameters, e.g. input files
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
14
16. ■ Patient: 63 years, male, smoker, chronic heart insufficiency, stage III-IV
1. Appointment I (pre-surgery): Acquire systemic patient details, e.g.
physiological and blood markers
2. Predict outcome using clinical model with patient specifics
3. Select adequate option and conduct valve replacement
4. Equip patient with sensors to allow regular monitoring
5. Appointment II 6 wks after surgery to validate outcome
Establish Systems Medicine Model for
Improved Treatment of Heart Failure
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
16
17. ■ Joint process definition
■ Identification of long running steps
■ Aims
□ Sharing of data
□ Improved communication
□ Reproducible data processing
□ Analysis applications for interactive
hypothesis validation
Requirements Engineering for System Medicine
Computer-aided Systems Medicine Process
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
17
18. ■ Structured data acquisition, e.g. IMDB as data integration platform
■ Improved communication, e.g. event-driven user notifications
■ Reproducible data processing, e.g. IMDB as processing platform for DNA
and RNA data
■ Enables real-time data analysis
Contributions
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
18
RNA Seq Analysis_V2
TopHat
Trimmomatic
FASTQC
STAR
featureCounts
Counts Matrix
BAM-File
Aligned Reads
FASTQC 2
FASTQ -
Trimmed Reads
Pre-Trimming
QC-Report
FASTQ - Reads
Post-Alignment
QC-Report
19. s
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
19
20. ■ Interdisciplinary partners collaborate on enabling interactive health research
■ Current 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)
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
20
21. ■ Analysis dashboard combining
functions per use case
■ Providing expert-facing entry
point to individual apps
■ Provides application-wide
authentication / single sign on
Interactive Analysis Dashboard
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
21
22. ■ Stratification of patient cohorts using patient specifics
■ Automatic matching of similar patients and patient anamnesis
■ Interactive graphical exploration of longitudinal patient data
Stratification of Hypertension Patients
and Longitudinal Data Analysis
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
22
23. ■ 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: A
Federerated In-
Memory Database for
Digital Health
23
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
24. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
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: A
Federerated In-
Memory Database for
Digital Health
Unified access to multiple formerly
disjoint data sources
Pathway analysis of genetic
variants with graph engine
24
25. Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
■ 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: A
Federerated In-
Memory Database for
Digital Health
25
26. ■ 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?
Learn more and test-drive it yourself: AnalyzeGenomes.com
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
26
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
27. Keep in contact with us!
Dr. Schapranow, BMBF
All Hands, Oct 11, 2017
Analyze Genomes: A
Federerated In-
Memory Database for
Digital Health
27
Dr.-Ing. 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/