Supporting The Virtual Physiological Human With Semantics And Services            Dr. Carlos Pedrinaci           KMi, The ...
Virtual Physiological       Human“... a methodological and technologicalframework that, once established, willenable colla...
(Some)      VPH Challenges• Data organisation and access• Integration and interpretation of  heterogeneous data• Creation ...
Components of a                               VPH Workflow    Clinical / /     Clinical                                   ...
Return            Users                  Select                         Retrieve       Infer missing            Run       ...
Creating aWeb of VPH Data
“Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”
Exposing VPH         Linked Data• Provisioning of modular vocabularies  for capturing patterns of data • Measurements, tre...
Outstanding issues   Simplicity vs Expressivity vs Support                     Controlled access to data                  ...
Using Linked Services    for the VPH
Linked Services• Linked Services are services described as  Linked Data (inputs, outputs, functionality...)   • That is, L...
Linked Services and VPH• Two main roles • Controlled publication of data as   Linked Data on demand • Supporting the creat...
Dealing with        Sensitive Data• Services for controlled access to the  data sources on demand • DBs, RESTful services,...
SupportingInfrastructure
Web of Documents                       Web of Data            http://iserve.kmi.open.ac.uk
Web of Documents                       Web of Data            http://iserve.kmi.open.ac.uk
Web of Documents                       Web of Data            http://iserve.kmi.open.ac.uk
Web of Documents                       Web of Data            http://iserve.kmi.open.ac.uk
“Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”
“Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”
Service Discovery• Simple SPARQL-based• Inputs/Outputs logic-based using  RDFS reasoning• Functional Classifications with R...
Linked Services       Invocation• Generic invocation engine OmniVoke • Based on declarative descriptions • RDF in, RDF out...
30
What’s Next?
Ongoing Research• Extension of workflow engine with  embedded Linked Services support• Improve assisted annotation• Cross-o...
Thanks for your       attention     Contact: c.pedrinaci@open.ac.ukThanks to: Guillermo Alvaro, Irene Celino,  John Doming...
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
Supporting the virtual physiological human with semantics and services e science 2011
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Supporting the virtual physiological human with semantics and services e science 2011

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Presentation on the use of Linked Services for supporting the Virtual Physiological Human, given at the Microsoft eScience workshop 2011

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Supporting the virtual physiological human with semantics and services e science 2011

  1. 1. Supporting The Virtual Physiological Human With Semantics And Services Dr. Carlos Pedrinaci KMi, The Open University
  2. 2. Virtual Physiological Human“... a methodological and technologicalframework that, once established, willenable collaborative investigation of thehuman body as a single complex system”
  3. 3. (Some) VPH Challenges• Data organisation and access• Integration and interpretation of heterogeneous data• Creation of composable and reusable analysis models• Automated evaluation of hypothesis or theories against available data
  4. 4. Components of a VPH Workflow Clinical / / Clinical Analysis Analysis Clinical Output Clinical Outputbiomedical data biomedical data Personalise VPH models Personalise VPH models Run simulations Run simulations Privacy, security, ethics Aggregate evidence, reduce uncertainty Support decisions for better • Select model(s ) • Infer missing items health outcomes • Images • Retrieve data from: • Estimate parameters • Diagnosis • Lab • literature • Integrate data • Genetic data • Treatment strategy • population data • boundary conditions • Lifestyle • Predictions • EHR, PHS • functional behaviours • ... • Prognosis • ... •... Exampleseu Heart Fit patient images Compute organ Patient Segment physiological function using Diagnostic index, suggestion Patient to virtual Diagnostic index, suggestion images biophysically based images population DB models of treatment strategy images models of treatment strategy Comparative Molecular dynamics Query DB, produce drug ranking DB simulation HIV genotypic assay molecular model HIV genotypic assay Literature: comparable Treatment suggestion of patient of mutated HIV Treatment suggestion of patient mutations w. HIV Drug ranking drug resistance
  5. 5. Return Users Select Retrieve Infer missing Run Results & Workflow Existing Data items simulation Support Patient Data Workflow Inputs Workflow Outputs VPH Outreach VPH-Share Project No: 269978 Co-ordinator:Application University of Patient Avatar Personalised Sheffield, UK Model Partners: CYFRONET, PL Sheffield Teaching Hospitals, UK ATOS Origin, ES Kings College London, UK Universitat Pompeu Fabra, ES Empirica, DE euHeart VPH OP SCS SRL, IT @neurIST Patient Centred Computational Workflows ViroLab NHS IC, UK INRIA, FR IOR, IT Open Univ., UK Philips Elec., NL TU Eindhoven, NL Univ. Auckland, NZ Knowledge Knowledge Discovery Decision Support Uv Amsterdam, NL UCL, UKInfostructure Management Data Inference Univ. Vienna, AT AATRM, ES FCRB, ES Data Services: Compute Services Storage Services Patient/Population HPC Infrastructure Cloud Platform (DEISA / PRACE) (Public / Private)
  6. 6. Creating aWeb of VPH Data
  7. 7. “Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”
  8. 8. Exposing VPH Linked Data• Provisioning of modular vocabularies for capturing patterns of data • Measurements, treatments, etc• Assisted annotation of services and DB2RDF mapping generation• Interlinking, mapping, indexing
  9. 9. Outstanding issues Simplicity vs Expressivity vs Support Controlled access to data Anonymisation of records Co-existence of different “unique IDs” for a single entity (e.g., patient) Large, heterogeneous, distributed, multi-party setting
  10. 10. Using Linked Services for the VPH
  11. 11. Linked Services• Linked Services are services described as Linked Data (inputs, outputs, functionality...) • That is, Linked Data describing reusable functionality• With supporting machinery Linked Services are Linked Data consumers and/or producers• Building blocks for Linked Data Applications
  12. 12. Linked Services and VPH• Two main roles • Controlled publication of data as Linked Data on demand • Supporting the creation of VPH workflows using Linked Services as processing activities
  13. 13. Dealing with Sensitive Data• Services for controlled access to the data sources on demand • DBs, RESTful services, Web Services• Services used to expose heterogeneous data as Linked Data on demand• Declarative descriptions cover how to deal with heterogeneous interfaces
  14. 14. SupportingInfrastructure
  15. 15. Web of Documents Web of Data http://iserve.kmi.open.ac.uk
  16. 16. Web of Documents Web of Data http://iserve.kmi.open.ac.uk
  17. 17. Web of Documents Web of Data http://iserve.kmi.open.ac.uk
  18. 18. Web of Documents Web of Data http://iserve.kmi.open.ac.uk
  19. 19. “Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”
  20. 20. “Linking Open Data cloud diagram, by Richard Cyganiak and Anja Jentzsch. http://lod-cloud.net/”
  21. 21. Service Discovery• Simple SPARQL-based• Inputs/Outputs logic-based using RDFS reasoning• Functional Classifications with RDFS reasoning, and over SKOS• Similarity analysis• Composition of these discovery types
  22. 22. Linked Services Invocation• Generic invocation engine OmniVoke • Based on declarative descriptions • RDF in, RDF out • Supports RESTful and Web services • Automated transformation of data • Injection of provenance data
  23. 23. 30
  24. 24. What’s Next?
  25. 25. Ongoing Research• Extension of workflow engine with embedded Linked Services support• Improve assisted annotation• Cross-ontology logic-based discovery of services
  26. 26. Thanks for your attention Contact: c.pedrinaci@open.ac.ukThanks to: Guillermo Alvaro, Irene Celino, John Domingue, Jacek Kopecky, Ning Li, Dong Liu, Maria Maleshkova

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