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BDE SC1 Workshop 3 - iASiS (Guillermo Palma)
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. 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
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. 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. 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
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
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
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
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