bde sc1 workshop 3 - iasis (guillermo palma)
TRANSCRIPT
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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
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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
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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)
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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
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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
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• 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
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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
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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
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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
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The iASiS Pipeline
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The iASiS Schema
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http://vocol.iais.fraunhofer.de/iasis/ Powered by VolCol
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The iASiS Pipeline
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The iASiS Architecture
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Knowledge GraphCentral Node
Node@Partner1Node@Partner2
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The iASiS Architecture
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Knowledge Graph
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The iASiS Architecture
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Knowledge GraphCentral Node
Node@Partner1
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The iASiS Architecture
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Knowledge GraphCentral Node
Node@Partner1Node@Partner2
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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
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Patterns between Drug-Protein Interactions
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Predicted Interactions between Drugs and Proteins
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Patterns between Drug and Side Effects
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Predicted Interactions between Drugs and Side Effects
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Conclusions and Future Work
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The iASiS Pipeline
The iASiS Architecture Big Data Analytics and Pattern Discovery
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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
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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
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Many thanks for your attention!
Questions?
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