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/

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
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http://project-iasis.eu
@Project_IASIS

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
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3. iASiS: Vision and Objectives
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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. Pilot 1: Lung Cancer
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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. 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. The iASiS Pipeline
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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. • 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. The iASiS Pipeline
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10. Clinical Big Data Analytics
Clinical
Notes NLP
Data
Preprocessing
Medical
Images
Deep Learning Predictive
Models
Medical
Vocabularies
Knowledge
Data Mining
Knowledge
Graph

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. 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. The iASiS Pipeline
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14. The iASiS Schema
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http://vocol.iais.fraunhofer.de/iasis/ Powered by VolCol

15. The iASiS Pipeline
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16. The iASiS Architecture
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Knowledge Graph
Central
Node
Node@Partner1
Node@Partner2

17. The iASiS Architecture
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Knowledge Graph

18. The iASiS Architecture
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Knowledge Graph
Central
Node
Node@Partner1

19. The iASiS Architecture
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Knowledge Graph
Central
Node
Node@Partner1
Node@Partner2

20. Big Data Analytics and Pattern Discovery
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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. Patterns between Drug-Protein Interactions
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Predicted Interactions between Drugs and Proteins

22. Patterns between Drug and Side Effects
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Predicted Interactions between Drugs and Side Effects

23. Conclusions and Future Work
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The iASiS Pipeline
The iASiS Architecture Big Data Analytics and Pattern Discovery

24. Conclusions and Future Work
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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. The iASiS Partners
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26. The iASiS Team
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27. Scientific Data Management Group at TIB
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Guillermo
Palma
Guillermo Betancourt Yerson Roa
Kemele
Endris
Farah
Karim
Bachelor StudentsPhD StudentsPostDoc
Maria-Esther Vidal
Samaneh
Jozashoori

28. Many thanks for your attention!
Questions?
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