Big Data in Life Sciences
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The document discusses the role of big data in life sciences, highlighting trends, challenges, and applications in precision medicine, particularly in oncology. It outlines the vast amounts of data generated from human genomes, clinical trials, and medical records, which contribute to informed decision-making for treatment options. It also promotes a platform for real-time analysis and data exchange to enhance personalized medicine.
Big Data in Life Sciences
- 1. Big Data in Life Sciences Dr. Matthieu-P. Schapranow CMS Global Life Sciences Forum, Frankfurt, Germany Nov 9, 2015
- 2. What is the Hasso Plattner Institute, Potsdam, Germany? Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 2
- 3. What are the Trends? Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 3 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
- 4. IT Challenges in Life Sciences Distributed Heterogeneous Data Sources Human genome/biological data >750GB per complete human genome >15PB in databases of leading institutes Prescription data 1.5B records from 10,000 doctors and 10M Patients (100 GB) Clinical trials >30k recruiting trials on ClinicalTrials.gov Human proteome 160M data points (2.4GB) per sample >3TB raw proteome data in ProteomicsDB PubMed database >24M unstructured data in publications Hospital information systems >50GB structured relational data Medical sensor data Scan of a single organ creates 10GB of raw data within 1s Cancer patient records >160k records only at NCT Big Data in Life Sciences Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Chart 4
- 5. Healthcare Interactions in the 21st Century Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 5 Indirect Interaction Direct Interaction C linician PatientResearcher Pharm aceutical Com pany H ealthcare Providers H ospital Research Center Laboratory Patient Advocacy G roup
- 6. Use Case: Precision Medicine in Oncology Identification of Best Treatment Option for Cancer Patient ■ Patient: 48 years, female, non-smoker, smoke-free environment ■ Diagnosis: Non-Small Cell Lung Cancer (NSCLC), stage IV 1. Surgery to remove tumor 2. Tumor sample is sent to laboratory to extract DNA 3. DNA is sequenced resulting in 750 GB of raw data per sample 4. Processing of raw data to perform analysis 5. Identification of relevant driver mutations using international medical knowledge 6. Informed decision making Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 6
- 7. Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 7
- 8. Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 8
- 9. Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 we.analyzegenomes.com Real-time Analysis of Big Medical Data 9 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 Big Data in Life Sciences Drug Response Analysis Pathway Topology Analysis Medical Knowledge CockpitOncolyzer Clinical Trial Recruitment Cohort Analysis ... Indexed Sources
- 10. Real-time Data Analysis and Interactive Exploration Drug Response Analysis Data Sources Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences Smoking status, tumor classification and age (1MB - 100MB) Raw DNA data and genetic variants (100MB - 1TB) Medication efficiency and wet lab results (10MB - 1GB) 10 Patient-specific Data Tumor-specific Data Compound Interaction Data
- 11. Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 11
- 12. Showcase Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 12 Calculating Drug Response…Predict Drug Response
- 13. Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 13 cetuximab might be more beneficial for the current case
- 14. ■ Online: Visit we.analyzegenomes.com for latest research results, slides, videos, tools, and publications ■ Offline: Read more about it, e.g. High-Performance In-Memory Genome Data Analysis: How In-Memory Database Technology Accelerates Personalized Medicine, In-Memory Data Management Research, Springer, ISBN: 978-3-319-03034-0, 2014 ■ In Person: Join us for “Festival of Genomics” Jan 19-21, 2016 in London, UK Where do you find additional information? Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 14
- 15. Keep in contact with us! Hasso Plattner Institute Enterprise Platform & Integration Concepts (EPIC) Program Manager E-Health August-Bebel-Str. 88 14482 Potsdam, Germany Dr. Matthieu-P. Schapranow schapranow@hpi.de http://we.analyzegenomes.com/ Schapranow, CMS Global Life Sciences, Fankfurt, Nov 9, 2015 Big Data in Life Sciences 15