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Una estrategia para la integración de ontologías, servicios web y PLN en el análisis de documentación científica

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Conferencia presentada por el Ing. José López, como parte del X Coloquio Internacional de Tecnologías aplicadas a los Serivicios de Información

Conferencia presentada por el Ing. José López, como parte del X Coloquio Internacional de Tecnologías aplicadas a los Serivicios de Información

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Una estrategia para la integración de ontologías, servicios web y PLN en el análisis de documentación científica Presentation Transcript

  • 1. Universidad Nacional Experimental del Tachira Decanato de Investigación Laboratorio de Computación de Alto Rendimiento-LCAR “ X Coloquio internacional sobre tecnologías aplicadas a los servicios de información”An approach to integrate ontologies, NLP and Web Services to analyze scientific documentation An application case: Apoptosis José López <jlopez@unet.edu.ve> Jacinto Dávila <jacinto@ula.ve> Dahyana Nimo <dahycar@gmail.com> Mary Carlota Bernal <marybernalj@gmail.com> Javier Maldonado <jamc2004@gmail.com> UNET, November 2010 UNET-LCAR
  • 2. Agenda• The apoptosis signaling network (the application case)• Extending an ontology.• Queries (types of).• Building the ontology.• What we search to reason about?• A proposal for an intelligent web system based on ontologies, NLPand Web Services• A first set of conclusions. UNET-LCAR
  • 3. Motivation To develop a web system able to deal with queries searching for possible relationships and interactions in the Apoptosis network map.But also... A platform able to support generic bio-molecular knowledge analysis from specialized documentation UNET-LCAR
  • 4. Apoptosis... ... or how a cell dies. UNET-LCAR
  • 5. The Apoptosis signaling network UNET-LCAR
  • 6. Why is useful a Molecular Interaction Map? •It is often difficult to keep in mind all of the known interactions •Molecular interaction maps can suggest new interpretations or questions for experiment. •The act of preparing a molecular interaction map imposes a discipline of logic and critique to the formulation of functional models •A diagram convention provides a shorthand for recording complicated findings or hypotheses. Kurt W. Kohn, Molecular Interaction Map of the Mammalian Cell Cycle Control and DNA Repair Systems, Mol. Biol. Cell, Vol. 10, Issue 8, 2703- 2734, August 1999 UNET-LCAR
  • 7. Queries guiding the BioPAX ontology extension Given a ligand, what type of protein-protein interactions result when it binds to a cognate receptor, and which of them lead to the up- regulation of a transcriptional response? Given a DNA motif, its related receptor(s) and (or) complex(es), which genes are transcriptional up- regulated or down-regulated by? Given a receptor and its associated complex(es), in which tissues are they related with a high (or low) transcriptional response for its targeted genes?. UNET-LCAR
  • 8. Adapting the BioPAX ontology … …. to represent the Apoptosis signalling network. Multiple organism-tissue specificities for protein or pathway descriptions. Synthesis To model DNA->Protein events isa and related pathway steps.Apoptosis Synthesis ACTIVATION-TRANSCRIPTIONAL STIMULATION-TRANSCRIPTIONAL INHIBITION-COMPETITIVE-TRANSCRIPTIONAL ACTIVATION- RECEPTOR INHIBITION-RECEPTOR The BioPAX Ontology (Local extended version) UNET-LCAR
  • 9. Building the Apoptosis digital representation using the BIOPAX ontology (Local extended version) Description for a particular complex Here a partial Apoptosis SN list of complexes. Protein instancesA set of entities representing a partial view of the Apoptosis SN UNET-LCAR
  • 10. Building the Human Reactome UNET-LCAR
  • 11. A first web application prototype to reason about a SN ontology Web SystemMIM uploading/downloading Web Application 1 Queries Ontology 2 Reasoner Answers (Relations). 3 4 HTML based user interface BioPAX-vE OWL Files 1. Consults to the reasoner to get answers for queries 2. Reasoner’s results to format as answers 3. BioPAX-vE Knowledge base updates and consults 4. Reasoner access to a particular BioPAX-vE file. XML data base BioPAX-vE: BioPAX version Extended UNET-LCAR
  • 12. A first goalA standard-compliant global map of the bile acid/xenobiotic signaling network: Construction and automated query processing. O. Schmidt1, J. López2, F. Azuaje3, P. Thompson1, M. Swain1, and W. Dubitzky1 1 Biomedical Sciences Research Institute , University of Ulster, Coleraine, Co. Londonderry, NI, UK . 2 Laboratorio de Computación de Alto Rendimiento, Universidad del Táchira, San Cristóbal, Edo. Táchira, Venezuela 3 Research Centre for Public Health (CRP-Santé), Cardiovascular Research Strassen, Luxembourg BIOCOMP09 The 2009 International Conference on Bioinformatics & Computational Biology Monte Carlo Resort, Las Vegas, Nevada, USA (July 13-16, 2009) UNET-LCAR
  • 13. How to reason about a signalling network?An Explanation: Given O which A is related with?A plan: To obtain p which A I have to follow?A prediction: Given A is possible p? Knowledge BaseA MIM related query Reasoner Explanations, (Inference Engine) Planning and Predictions UNET-LCAR
  • 14. An intelligent web system to represent and reason about a signaling network 7 Web server 6 MIM Ontology Edition? MIM Local XML Edition? MIM SBML Edition? 6 MIM uploading/downloading 1 Models and KBs repository Queries Explanations, XML, OWL data base Planning and Predictions. 2 2 User interface1. Digital representation management 5 KB Builder/Monitor 3 Knowledge Base2. Knowledge base building and management3. knowledge base delivering and monitoring4. Knowledge base consults and updates Reasoner 45. Reasoner answers6. Access to repositories of models 47. Web services based access to models and KBs UNET-LCAR
  • 15. An architecture for a summarizer UNET-LCAR
  • 16. OntologiesKB Builder(Subject, Relation, Object) Producer Ontology Automatic Ontology Translation Annotation To ontologies repository To general purpose KB UNET-LCAR
  • 17. An adapted dictionary for MIM analysis UNET-LCAR
  • 18. The kind of output from the summarizer UNET-LCAR
  • 19. Conclusions (…at this time)• The Ontologies defines a format for qualitative analysis and informationsharing.• The ontology used imposes an organized and detailed description aboutthe knowledge domain.• The manual annotation must be supported by some kind of automaticprocess.• The summarizer has been connected with a knowledge domain using theontology producing relevant summaries.• An approach to deal with general logic analysis and automatic ontologyannotation had been proposed.• A standard procedure must be implemented to connect the architectureproposed with relevant APIs and Web Services.• A similar approach coul be followed with other knowledge domains usingontologies and other methods to produce specialized lexical. UNET-LCAR
  • 20. References1 Metabolism - Basic introduction to metabolism. The Virtual Library of Biochemistry, Moleculer Biology and Cell Biology. http://www.biochemweb.org/.2 Timothy T. Lu, Makoto Makishima, Joyce J. Repa, Kristina Schoonjans, Thomas A. Kerr, Johan Auwerx, and David J. Mangelsdorf. Molecular Basis for Feedback Regulation of Bile Acid Synthesis by Nuclear Receptors. Molecular Cell, Vol. 6, 507–515, September, 2000.3 Molecular Interaction Maps. The Genomics and Bioinformatics Group. http://discover.nci.nih.gov/mim/index.jsp4 Kurt W. Kohn, Molecular Interaction Map of the Mammalian Cell Cycle Control and DNA Repair Systems, Mol. Biol. Cell, Vol. 10, Issue 8, 2703-2734, August 1999.5 Hiroaki Kitano, Akira Funahashi, Yukiko Matsuoka1, Kanae Oda. Using process diagrams for the graphical representation of biological networks, Nature Biotechnology 23(8), 961 - 966 (2005).6 Systems Biology Markup Language (SBML) Level 2: Structures and Facilities for Model Definitions, 2003. http://sbml.org/documents/.7 BioPAX – Biological Pathways Exchange Language. Level 2, Version 1.0 Documentation, 2005. http://www.biopax.org/8 CellDesigner.org. http://www.celldesigner.org/9 Protégé. The de facto standard for editing OWL. http://protege.stanford.edu/10 Catherine M. Lloyd et al; CellML its future, present and past; Progress in Biophysics & Molecular Biology 85 (2004) 433– 450.11 Baral et al; A knowledge based approach for representing and reasoning about signaling networks;Bioinformatics, Vol. 20 Suppl. 1 2004, pages 115–122.12 Mindswap: Maryland Information and Network Dynamics Lab Semantic Web Agents Project http://www.mindswap.org/2003/pellet/index.shtml UNET-LCAR
  • 21. Questions? UNET-LCAR