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Ontrez

Ontrez. Clement Jonquet – Nigam Shah { jonquet,nigam }@ stanford.edu. Speech overview. Ontrez general idea “provide a service that will enable users to locate biomedical data resources related to their search for particular ontology terms” Functional specification and conceptual levels

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Ontrez

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  1. Ontrez Clement Jonquet – Nigam Shah {jonquet,nigam}@stanford.edu

  2. Speech overview • Ontrez general idea • “provide a service that will enable users to locate biomedical data resources related to their search for particular ontology terms” • Functional specification and conceptual levels • Ontrez example • GEO dataset • Architecture and implementation • UML class diagrams • Ontrez position within NCBO project • Relation with OBD@Berkeley and OBD@Stanford • Next steps & conclusion

  3. Classic Ontrez use case A user search for information and content related to a specific disease… • Go to BioPortal to search ontologies… • The given disease name matches with several ontology terms… • For each of this term, the user get a link to all the resource elements (data sets, clinical trial, articles) annotated by this term.

  4. Ontrez challenge • Biomedical resource elements (e.g., experiments and data) in the public domain are exploding • Element = a collection of observations resulting from a biomedical experiment (experimental data sets, records of disease associations of gene products in mutation databases, entries of clinical-trial descriptions, etc.) • Resource = a collection of elements (GEO, PubMed, or other public repositories) • Researchers need tools to enable them to find all the resource elements relevant to their area of study • The problem now is locating the ‘elements’ that matters to a user. • Key challenge is to annotate (or tag) various resource elements to identify the biomedical concepts to which they relate • Annotation = an assertion declaring a relationship between a biomedical resource elements and a term in an ontology • Term = concept found in an specific ontology

  5. Creation of the annotation database Ontrez proposal • Retrieve the metadata from data resources (A) • Annotate/tag them with ontology terms using the library of ontologies in Bioportal (B) • Store the result in an annotation database

  6. Query of the annotation databaseOntrez Proposal • User queries are formu--lated as a set of terms (1) • Use of the BioPortal index to convert the query to ontology terms • Use the subsumption relations in the ontologies and the mappings in BioPortal to expand the query • Query the annotation tables with the expanded set of terms (2) • The user receives the result (3) in terms of references to the original data sources.

  7. Ontrez conceptual levels

  8. Ontrez functional specification • For a given resource, being able to: • access and update automatically resource information • access and keep locally the set of elements of this resource • automatically update the local copy if necessary • extract the structure of elements of this resource • For a given element, being able to: • extract (according to the structure) the metadata • annotate each part of the metadata with a dictionary • For a given set of ontologies, being able to: • construct a dictionary • For a given annotation, being able to: • process the transitive closure for a given set of ontologies • link back to the original resource element • For a given term, being able to: • get the resources elements annotated with this term • semantically expand to a larger relevant set of terms

  9. Ontrez example (1/2) Annotation of a GEO element

  10. Ontrez example (2/2) Return of a GEO element

  11. Ontrez in the new BioPortal prototype Example of resource available (name and description) Number of resource elements annotated with this ontology term Ontology term search by a user URL link to the original element Context in which an element has been annotated ID of an element

  12. Ontrez v.0 architecture (1/3)Concept Level

  13. Ontrez v.0 architecture (2/3)Resource Level

  14. Ontrez v.0 architecture (3/3)Index Level

  15. Ontrez implementation • Construction of the index: Java • Storage of resource elements, dictionaries, and … annotations: MySQL • Connexion to ontologies for dictionary construction and semantic query expansion: • Web services for Bioportal; • JDBC connection to MySQLDB for UMLS

  16. Concept recognition tools • University of Michigan mgrep tool • National Center for Integrative Biomedical Informatics (NCIBI) • Has a very high degree of accuracy (over 95%) in recognizing disease names • RongXu’s • BMIR PhD candidate • MetaMap Transfer (MMTX) • We have not yet conducted an evaluation • Nipun Bathia’s project BS student at Stanford

  17. First resources processed(Ontrez v0 and v1) • 5 resources for the moment (not complete) • To be completed and done with versioning, automatic updates, etc.

  18. Open Biomedical Data (OBD) NCBO Core 2 • “a database resource (…) • “that will allow expert scientists both to archive experimental data and to use the OBO ontologies and terminologies to create appropriate annotations” • CREATION • “for storing, visualizing, and analyzing the ontology-based annotations that are linked to primary experimental data” • USE

  19. Different annotation sets in OBD • OBD@stanford: • “disease oriented approach” • automatically generated annotations of text meta-data • OBD@Berkeley: • “genotype-phenotype pairsapproach” • manually generated and curator based annotations/assertions

  20. Interaction with and integration into Bioportal ? • We don’t really care one another about how these DBs are produced • Manually, with curators etc. on Berkeley side • Using a NLP tool on text meta-data on Stanford side • We have several annotations databases (Ontrez/OBD/external ones) for which we need to specify: • Annotation table structure i.e., [ elementLocalID | termID ] • Interaction with Bioportal to: • get the term IDs; • request the DB and integrating results in UI. Web services API .jar ? ? [entryID | elementLocalID | termID | itemKey | dictionaryVer]

  21. Next steps & conclusion • Resources to process completely and versioning and update mechanisms to implement • First evaluation/comparison • Research and implementation of semantic query expansion • New resources to be added • Formalization with Berkeley folks of OBD@ncbo • Results integration within BioPortal • Collaboration on what to do with annotations

  22. Thank you Any questions?

  23. Who am I? Clement, 27, French Post doc on NCBO since September 2007 at Stanford University • PhD in Informatics • University Montpellier 2 (FR) • Thesis • Multi-Agents Systems • Service oriented computing • Grid • Nothing to go with biomedical ontologies but… • www.stanford.edu/~jonquet

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