Big Data Analytics in the Health Domain
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The document discusses the integration and analysis of big data in the context of precision medicine and public health, highlighting studies on drug side effects, Alzheimer's disease, and lung cancer. It emphasizes the need for scalable methods in data collection and analysis to support personalized healthcare through actionable knowledge. The iasis framework aims to transform clinical and genomic data into effective medical decisions tailored for individual patients.
![Big Data Analytics and Drug Side Effects
2
Big Data Analytics study has found an association
between the use of proton-pump inhibitors and the
likelihood of incurring a heart attack
[Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.]
Heart Attack
Electronic Health Records (EHRs) of nearly 3 million
people and trillions of pieces of medical data
Big Data](https://image.slidesharecdn.com/bigdataanalyticsbde-171129112052/85/Big-Data-Analytics-in-the-Health-Domain-2-320.jpg)
![Drug Ineffectiveness
11
[Source: Brian B. Spear, Margo Heath-Chiozzi, Jeffrey Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, pages 201-204.]](https://image.slidesharecdn.com/bigdataanalyticsbde-171129112052/85/Big-Data-Analytics-in-the-Health-Domain-11-320.jpg)
![Drug Side Effects
13
https://www.topcanadianpharmacy.org/wp-content/uploads/2016/09/Plavix-package.jpg
Drug designed to prevent blood clots
The second-best-selling drug in the world
Different impact on protecting stent patients
from thrombosis depending on patient genetic
variance within CYP2C19
CYP2C19 encodes an enzyme that converts the
drug from an inactive to an active state.
[Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.]
The right drug and dosage are
selected based on a patient
genome](https://image.slidesharecdn.com/bigdataanalyticsbde-171129112052/85/Big-Data-Analytics-in-the-Health-Domain-13-320.jpg)
![Supervised Machine Learning Approaches
48
Machine Learning for Drug-Target Interaction Discovery:
• BLM: Bipartite Local Method [Cheng et al]
• LapRLS: Laplacian Regularized Least Squares [Xia et al]
• GIP: Gaussian Interaction Profile [Van Laarhoven et al]
• KBMF2K: Kernelized Bayesian Matrix Factorization with
twin Kernels [Gonen]
• NBI: Network-Based Inference [Cheng et al]](https://image.slidesharecdn.com/bigdataanalyticsbde-171129112052/85/Big-Data-Analytics-in-the-Health-Domain-48-320.jpg)
![Unsupervised Approaches
49
• semEP: Graph Partition into communities from where new
interactions are predicted [Palma, Vidal, and Raschid]
• Metis: Multilevel recursive-bisection [George Karypis and
Vipin Kumar]
• Ncut: Normalized Cuts [Shi and Malik]](https://image.slidesharecdn.com/bigdataanalyticsbde-171129112052/85/Big-Data-Analytics-in-the-Health-Domain-49-320.jpg)
Big Data Analytics in the Health Domain
- 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 Big Data Analytics Maria-Esther Vidal Leibniz Information Centre For Science and Technology University Library (TIB), Germany 06.11.2017 BigDataEurope Workshop on Big Data in Climate Action, Environment, Resource Efficiency and Raw Materials 1 http://project-iasis.eu @Project_IASIS
- 2. Big Data Analytics and Drug Side Effects 2 Big Data Analytics study has found an association between the use of proton-pump inhibitors and the likelihood of incurring a heart attack [Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.] Heart Attack Electronic Health Records (EHRs) of nearly 3 million people and trillions of pieces of medical data Big Data
- 3. Big Data Analytics and Disease Predisposition Researchers analyzed more than 7,700 brain images from 1,171 people in various stages of Alzheimer's progression using a variety of techniques including magnetic resonance imaging (MRI) and positron emission tomography (PET). Alzheimer’s Disease Evolution Big Data Analytics study has found that changes in blood flow are the earliest known warning sign of Alzheimer's. Big Data https://www.sciencedaily.com/releases/2016/07/160712130229.htm 3
- 4. Big Data: Medical Image Large volumes of data produced by Imaging Techniques Volume of medical images is growing exponentially Annotated data or structured methods to annotate medical images is challenging Big Data Analytical Methods allow for the interpretability of depicted contents Scalable Methods for collecting, compressing, sharing, and anonymizing medical data Medical Image Processing from Big Data Point of View 4
- 5. Electronic Health Records (EHR) from Big Data Point of View Big Data: Clinical Data Large volumes of Clinical Data that needs to be stored, retrieved, and aggregated Scalable Methods for collecting, compressing, sharing, and anonymizing medical data Scalable Methods for signal processing and for developing Big Data based clinical decision support systems (CDSSs) 5
- 6. Genomics from Big Data Point of View Big Data: Genomics Data Human Genome consists of 30,000 to 35,000 genes Scalable Methods for pathway analysis and for the discover associations between observed gene expression changes and predicted functional effects Scalable Methods for Reconstruction of Metabolic and Regulatory Networks 6
- 7. 7 Challenges of Big Data Management and Analytics
- 8. 8 Challenges of Big Data Management and Analytics
- 10. Agenda 1. iASiS: Big Data to Support Precision Medicine and Public Health Policy 2. The iASiS Architecture 3. Big Data Management and Analytics in iASiS 10
- 11. Drug Ineffectiveness 11 [Source: Brian B. Spear, Margo Heath-Chiozzi, Jeffrey Huff, “Clinical Trends in Molecular Medicine,” Volume 7, Issue 5, 1 May 2001, pages 201-204.]
- 12. Drug Effectiveness 12 https://healthy.kaiserpermanente.org/static/drugency/images/ACI02770.JPG Methotrexate is a chemotherapy agent and immune system suppressant Drugs Approved for Small Cell Lung Cancer https://www.cancer.gov/about-cancer/treatment/drugs/lung The right drug and dosage are selected based on a patient genome
- 13. Drug Side Effects 13 https://www.topcanadianpharmacy.org/wp-content/uploads/2016/09/Plavix-package.jpg Drug designed to prevent blood clots The second-best-selling drug in the world Different impact on protecting stent patients from thrombosis depending on patient genetic variance within CYP2C19 CYP2C19 encodes an enzyme that converts the drug from an inactive to an active state. [Shah, SH. Clopidogrel Dosing and CYP2C19. Medscape. July 1, 2011. Medscape web site.] The right drug and dosage are selected based on a patient genome
- 14. Precision Medicine 14 Medical model that proposes the customization of healthcare, with medical decisions, practices, or products being tailored to the individual patient
- 15. Precision Medicine 15 Stratified Medicine Targeted Therapy Personalized Medicine P4 Medicine: Personalized, Predictive, Preventive, Participatory Medical model that proposes the customization of healthcare, with medical decisions, practices, or products being tailored to the individual patient
- 16. iASiS: Vision and Objectives 16 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
- 20. The iASiS Schema 20 http://vocol.iais.fraunhofer.de/iasis/ Powered by VolCol
- 22. Pilot 1: Lung Cancer 22 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)
- 23. 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
- 24. 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
- 25. • 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
- 26. Clinical Big Data Analytics Clinical Notes NLP Data Preprocessing Medical Images Deep Learning Predictive Models Medical Vocabularies Knowledge Data Mining Knowledge Graph
- 27. 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
- 28. 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
- 29. Agenda 1. iASiS: Big Data to Support Precision Medicine and Public Health Policy 2. The iASiS Architecture 3. Big Data Management and Analytics in iASiS 29
- 30. The iASiS Architecture 30 Knowledge Graph
- 31. The iASiS Architecture 31 Knowledge Graph Central Node Node@Partner1
- 32. The iASiS Architecture 32 Knowledge Graph Central Node Node@Partner1 Node@Partner2
- 33. Agenda 1. iASiS: Big Data to Support Precision Medicine and Public Health Policy 2. The iASiS Architecture 3. Big Data Management and Analytics in iASiS 33
- 34. Big Data Management & Analytics 34
- 35. Big Data Management & Analytics 35
- 36. Big Data Management & Analytics 36
- 37. Big Data Management & Analytics 37
- 38. Ontario: Big Data Management 38 Knowledge Graph
- 40. Ontario: Evaluation Study 40 Benchmark by Ali Hasnain et. al BioFed: federated query processing over life sciences linked open data. Journal of Biomedical Semantics 2017. RDF Dataset Name Number of RDF Triples Chebi 4,772,706 DrugBank 517,023 Kegg 1,090,830 Affymetrix 44,207,146 Dailymed 162,972 Diseasome 72,445 Sider 101,542 Medicare 44,500 LinkedCT 9,804,652 Linked TCGA-A 35,329,868
- 41. Ontario: Evaluation Study 41 Benchmark by Ali Hasnain et. al BioFed: federated query processing over life sciences linked open data. Journal of Biomedical Semantics 2017. Category #Triple Patterns #Star-Shaped Subqueries #Union #Optional Min Max Min Max Min Max Min Max Simple Queries 3 8 2 4 0 1 0 1 Complex Queries 6 12 2 5 0 1 0 1
- 42. Ontario: Evaluation Study 42 • Ontario is compared with state-of-the-art federated query processing tools (ANAPSID and FedX) and direct SPARQL endpoint • Ontario exhibits better performance than the results of the engines in terms of throughput
- 43. Ontario: Evaluation Study 43 • Ontario is compared with state-of-the-art federated query processing tools (ANAPSID and FedX) and direct SPARQL endpoint • Ontario exhibits better performance than the results of the engines in terms of throughput
- 44. Big Data Analytics and Pattern Discovery 44 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
- 45. Patterns between Drug-Protein Interactions 45 Predicted Interactions between Drugs and Proteins
- 46. Patterns between Drug and Side Effects 46 Predicted Interactions between Drugs and Side Effects
- 47. Drug-Target Interaction Discovery 47 Nuclear Receptor GPCR Ion Channel Enzym e Drugs 54 223 210 445 Targets 26 95 204 664 Interactions 90 635 1,476 2,926 Avg. Interaction per Target 3.46 6.68 7.23 4.4 Avg. Interaction per Drug 1.66 2.84 7.02 6.57
- 48. Supervised Machine Learning Approaches 48 Machine Learning for Drug-Target Interaction Discovery: • BLM: Bipartite Local Method [Cheng et al] • LapRLS: Laplacian Regularized Least Squares [Xia et al] • GIP: Gaussian Interaction Profile [Van Laarhoven et al] • KBMF2K: Kernelized Bayesian Matrix Factorization with twin Kernels [Gonen] • NBI: Network-Based Inference [Cheng et al]
- 49. Unsupervised Approaches 49 • semEP: Graph Partition into communities from where new interactions are predicted [Palma, Vidal, and Raschid] • Metis: Multilevel recursive-bisection [George Karypis and Vipin Kumar] • Ncut: Normalized Cuts [Shi and Malik]
- 50. Drug-Target Interaction Discovery 50 Top5 Novel interactions are interactions that do not appear in the dataset and can be validated in STITCH (http://stitch.embl.de/) and KEGG (http://www.kegg.jp ) Method Nuclear Receptor GPCR Ion Channel Enzyme semEP 5 5 4 1 Metis 3 5 2 1 Ncut 3 4 1 1 BLM 2 1 0 0 NBI 1 1 1 2 GIP 4 2 1 3 LapRLS 4 4 2 2 KBMF2K 4 4 4 4
- 51. Conclusions and Future Work 51 Biomedicine Big Data The iASiS Pipeline The iASiS Architecture Big Data Management and Analytics
- 52. Conclusions and Future Work 52 Biomedicine Big Data The iASiS Pipeline The iASiS Architecture Big Data Management and Analytics Next Steps: ● Collect Big Data ● Extract Knowledge from Big Data sources ● Create the iASiS Knowledge Graph ● Enforce Data Access and Privacy Policies ● Big Data Processing and Analytics
- 55. Scientific Data Management Group at TIB 55 Guillermo Palma Guillermo Betancourt Yerson Roa Kemele Endris Farah Karim Bachelor StudentsPhD StudentsPostDoc Maria-Esther Vidal
- 56. Many thanks for your attention! Questions? 56