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BDE SC1 Workshop 3 - iASiS (Guillermo Palma)

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Overview of the iASiS project presented at BDE SC1 Workshop 3, 13 December, 2017.

https://www.big-data-europe.eu/the-final-big-data-europe-workshop/
http://project-iasis.eu/

Published in: Healthcare
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BDE SC1 Workshop 3 - iASiS (Guillermo Palma)

  1. 1. Integration and analysis of heterogeneous big data for precision medicine and suggested treatments for different types of patients. SC1-PM-18-2016: Big Data supporting Public Health policies iASiS: Big Data to Support Precision Medicine and Public Health Policy Sören Auer, Guillermo Palma, Maria-Esther Vidal Farah Karim, Kemele Endris, Samaneh Jozashoori, Scientific Data Management Group 13.12.2017 Big Data Europe: 3rd Workshop on Big Data in Health 1 http://project-iasis.eu @Project_IASIS
  2. 2. Agenda 1. iASiS: Big Data to Support Precision Medicine and Public Health Policy 2. The iASiS Architecture 3. iASiS Big Data Analytics and Pattern Discovery 2
  3. 3. iASiS: Vision and Objectives 3 iASiS Vision: Turn clinical, pharmacogenomics, and other Big Data into actionable knowledge for personalized medicine and health policy-making iASiS Objectives: •Integrate automated unstructured and structured data analysis, image analysis, and sequence analysis into a Big Data framework •Use the iASiS framework to support personalized diagnosis and treatment
  4. 4. Pilot 1: Lung Cancer 4 Motivation: • Lung cancer among the most • common and deadly diseases • costly cancers • Lung cancer is a heterogeneous disease. Characteristics differ among • patients • tumor regions iASiS will enable: • Discovery of correlations among tumor spread, prognosis, response to treatment • Unraveling molecular mechanisms that predict response to different tumor types (signatures)
  5. 5. Pilot 2: Alzheimer's Disease Motivation: • Approximately, 10% of people over 65 suffer from Alzheimer’s • Heterogeneity of the symptoms impedes accurate diagnosis and treatments iASiS will enable: • Discovery of patterns associated with prognosis, outcomes, and response to treatments • Association of medical and lifestyle advice to Alzheimer’s risk and stages of severity
  6. 6. The iASiS Pipeline 6
  7. 7. Big Data in the Lung Cancer Pilot • Pharmacological knowledge extracted from publicly available datasets • Biomedical ontologies and taxonomies • terminology standardization • semantically describing the EHRs • EHRs in Spanish • PET/CT Images • Genomic Data/Liquid Biopsy Samples
  8. 8. • EHRs in English • MRI Brain Images • Genomic Data • Pharmacological knowledge extracted from publicly available datasets • Biomedical ontologies and taxonomies • terminology standardization • semantically describing the EHRs Big Data in the Alzheimer's Disease Pilot
  9. 9. The iASiS Pipeline 9
  10. 10. Clinical Big Data Analytics Clinical Notes NLP Data Preprocessing Medical Images Deep Learning Predictive Models Medical Vocabularies Knowledge Data Mining Knowledge Graph
  11. 11. Genomic Big Data Analytics Identification of RNAs regulated by RBP Comparison with available information Integration with transcriptomic data Hospital-derived data Knowledge Graph Identification of key genes and interactions
  12. 12. Open Big Data Analytics • Heterogeneous open data • Semantic indexing of the data via ontologies and thesauri • Knowledge extraction from the data • NLP and network analysis technologies
  13. 13. The iASiS Pipeline 13
  14. 14. The iASiS Schema 14 http://vocol.iais.fraunhofer.de/iasis/ Powered by VolCol
  15. 15. The iASiS Pipeline 15
  16. 16. The iASiS Architecture 16 Knowledge Graph Central Node Node@Partner1 Node@Partner2
  17. 17. The iASiS Architecture 17 Knowledge Graph
  18. 18. The iASiS Architecture 18 Knowledge Graph Central Node Node@Partner1
  19. 19. The iASiS Architecture 19 Knowledge Graph Central Node Node@Partner1 Node@Partner2
  20. 20. Big Data Analytics and Pattern Discovery 20 Pragmatics ContextSemantics Drug Similarity Target Similarity Detecting Patterns Predicting Interactions Computing Similarity Pattern Detection Prediction Principle Network of Drugs, Targets, and interactions Patterns of similar Drugs, similar Targets, and their interactions Discovered Drug-Target Interactions ChEBI Ontology
  21. 21. Patterns between Drug-Protein Interactions 21 Predicted Interactions between Drugs and Proteins
  22. 22. Patterns between Drug and Side Effects 22 Predicted Interactions between Drugs and Side Effects
  23. 23. Conclusions and Future Work 23 The iASiS Pipeline The iASiS Architecture Big Data Analytics and Pattern Discovery
  24. 24. Conclusions and Future Work 24 The iASiS Pipeline The iASiS Architecture Big Data Analytics and Pattern Discovery Next Steps: ● Ingest Big Data ● Extract Knowledge from Big Data sources ● Create the iASiS Knowledge Graph ● Enforce Data Access and Privacy Policies ● Big Data Processing and Analytics
  25. 25. The iASiS Partners 25
  26. 26. The iASiS Team 26
  27. 27. Scientific Data Management Group at TIB 27 Guillermo Palma Guillermo Betancourt Yerson Roa Kemele Endris Farah Karim Bachelor StudentsPhD StudentsPostDoc Maria-Esther Vidal Samaneh Jozashoori
  28. 28. Many thanks for your attention! Questions? 28

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