bde sc1 workshop 3 - iasis (guillermo palma)
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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 VidalFarah 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
Agenda
1. iASiS: Big Data to Support Precision Medicine and Public Health Policy
2. The iASiS Architecture3. iASiS Big Data Analytics and Pattern Discovery
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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 andtreatment
Pilot 1: Lung Cancer
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Motivation:
• Lung cancer among the most• common and deadly diseases • costly cancers
• Lung cancer is a heterogeneousdisease. 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)
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 withprognosis, outcomes, and response to treatments
• Association of medical and lifestyleadvice to Alzheimer’s risk and stages of severity
The iASiS Pipeline
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Big Data in the Lung Cancer Pilot
• Pharmacological knowledge extractedfrom publicly available datasets
• Biomedical ontologies and taxonomies• terminology standardization• semantically describing the EHRs
• EHRs in Spanish
• PET/CT Images
• Genomic Data/Liquid Biopsy Samples
• EHRs in English
• MRI Brain Images
• Genomic Data
• Pharmacological knowledge extractedfrom publicly available datasets
• Biomedical ontologies and taxonomies• terminology standardization• semantically describing the EHRs
Big Data in the Alzheimer's Disease Pilot
The iASiS Pipeline
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Clinical Big Data Analytics
Clinical Notes NLP
Data Preprocessing
Medical Images
Deep Learning Predictive Models
Medical Vocabularies
Knowledge
Data Mining
Knowledge Graph
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
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
The iASiS Pipeline
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The iASiS Schema
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http://vocol.iais.fraunhofer.de/iasis/ Powered by VolCol
The iASiS Pipeline
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The iASiS Architecture
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Knowledge GraphCentral Node
Node@Partner1Node@Partner2
The iASiS Architecture
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Knowledge Graph
The iASiS Architecture
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Knowledge GraphCentral Node
Node@Partner1
The iASiS Architecture
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Knowledge GraphCentral Node
Node@Partner1Node@Partner2
Big Data Analytics and Pattern Discovery
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Pragmatics
ContextSemanticsDrug Similarity
TargetSimilarity
DetectingPatterns
PredictingInteractions
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
Patterns between Drug-Protein Interactions
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Predicted Interactions between Drugs and Proteins
Patterns between Drug and Side Effects
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Predicted Interactions between Drugs and Side Effects
Conclusions and Future Work
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The iASiS Pipeline
The iASiS Architecture Big Data Analytics and Pattern Discovery
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
The iASiS Partners
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The iASiS Team
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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
Many thanks for your attention!
Questions?
28
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