1 / 34

Adverse Events Following Immunization

Adverse Events Following Immunization Reporting s tandardization, automatic c ase c lassification and signal d etection Mélanie Courtot, Ryan R. Brinkman and Alan Ruttenberg. http:// purl.obolibrary.org /aero.

aluz
Download Presentation

Adverse Events Following Immunization

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Adverse Events Following Immunization Reporting standardization, automatic case classification and signal detection Mélanie Courtot, Ryan R. Brinkman and Alan Ruttenberg http://purl.obolibrary.org/aero

  2. Goal: Increase the timeliness and cost effectiveness of reliable adverse event signal detection Problem: Lack of standards for adverse events reporting limits ability to query and assess safety issues ADVERSE EVENTS REPORTS MANUAL REVIEW http://purl.obolibrary.org/aero

  3. Solution: • Translate existing paper-based case definitions into a computer amenable format, the Adverse Event Reporting Ontology, AERO • Apply AERO to current reporting systems data • Detect reliable safety signal • Faster, cheaper ADVERSE EVENTS REPORTS • Signal Detection • Curators • Policy Makers • Automated • classification http://purl.obolibrary.org/aero

  4. ADO: A disease ontology representing the domain knowledge specific to Alzheimer’s disease Ashutosh Malhotra

  5. Alzheimer’s disease ontology • Scientific knowledge was represented in the from of ADO based on standard life cycle of ontology building. • Current draft contains 1565 concepts and 2128synonyms

  6. Application • Mining 650 Electronic health records of AD patients. • Co-morbidity investigation Diabetes Hypertension Alzheimer’s disease Head Trauma Stroke

  7. IDODEN: An Ontologyfor DengueElvira Mitraka1,2, Pantelis Topalis1, Emmanuel Dialynas1, Vicky Dritsou1and Christos Louis1,2 1Institute ofMolecularBiologyand Biotechnology, Foundationfor Research and Technology Hellas, Crete, Greece2Department ofBiology, University ofCrete, Heraklion, Crete, Greece University of Crete Department of Biology National Institute of Allergy and Infectious Diseases VectorBase

  8. Severe Dengue is a leading cause of serious illness and death in some Asian and Latin American countries. There is no specific treatment for dengue. The Dengue Ontology (IDODEN) is being build according to the guidelines of the OBO-Foundry and is based on BFO. Rather than creating duplicate terms from existing ontologies, it imports those in accordance to the MIREOT rule set.

  9. Section regarding vaccine trials with terms imported from the Vaccine Ontology. Interoperability and cross-talk between existing databases.

  10. miRNAO: An Ontology for microRNAs Vicky Dritsou1, Pantelis Topalis1, Emmanuel Dialynas1, Elvira Mitraka1,2 and Christos Louis1,2 1Institute of Molecular Biology and Biotechnology, Foundation for Research and Technology - Hellas, Greece2Department of Biology, University of Crete, Greece

  11. Look, Dicer cleaves it! Wow, a pre-miRNA! .. and the mature miRNA And here’s the miRNA.. I see another transcript! Ha ha, a victim appeared!! Mine has been repressed.. Oh no, the miRNA destroyed it.. I’ll keep track of the repression in mine I’ll keep track of the degradation in my db

  12. But, we could use an ontology to classify the information! Then we could share knowledge!!! Because we don’t use a common schema! And I don’t know what you mean by the names you gave to the tables! Why don’t we share the knowledge stored in our dbs? We can’t exchange knowledge, impossible.. I’ve heard that some people from IMBB have a poster about an ontology for miRNAs! A few hours later… We ‘ve found the solution: The miRNA Ontology!!!

  13. Mapping of glossary terms from the Flora of North America to the Plant Ontology enhances both resources Ramona L. Walls*, Hong Cui, James A. Macklin, Chris Mungall, Laurel D. Cooper, Dennis W. Stevenson, and PankajJaiswal

  14. “…Leaves usually alternate or opposite, sometimes in basalrosettes, rarely in whorls; rarely stipulate, usually petiolate, sometimes sessile…”

  15. sporangium base PO:0030040 stem base PO:0008039 leaf lamina base PO:0008019

  16. ChEBI Ontology – ICBO 2012 Modular Extensions and Recent Developments Janna Hastings, EBI Cheminformatics and Metabolism

  17. Modular Extensions Error detection (chebi-disjoints.owl) Species, Disease (natural products, drugs)

  18. New Visualization, Annotation New visualization of hierarchy and relationships Annotation focus on Natural Products (metabolites)

  19. The Ontological Challenge of Laterality Jörg Niggemann CompuGroup Medical, Koblenz, Germany

  20. Nose Hand BodyPart ?

  21. BodyPart is-a Hand PairedStructure Left Hand Right Hand

  22. GOCI: An Ontology-Driven Search and CurationInfrastructure for the NHGRI GWAS Catalog

  23. Going from…

  24. … to

  25. Analyzing Tools for Biomedical Text Annotation with Multiple Ontologies Kele T. Belloze1, Daniel Igor S. B. Monteiro², Túlio F. Lima², Maria Claudia Cavalcanti2, Floriano P. Silva-Jr1 1Laboratory ofBiochemistryofProteinsandPeptides Oswaldo Cruz Institute, FIOCRUZ, Rio de Janeiro-RJ, Brazil. 2Department ofComputerScience MilitaryInstituteofEngineering, Rio de Janeiro-RJ, Brazil.

  26. Context • Textual databases such as PubMed • source for the extraction of useful information • Long texts, large digital libraries • Ontology-based semantic annotation • scientific articles of biomedical area often include information about different domains • ontologies are usually focused on a specific domain • Semantic annotation with multiple ontologies • Semantic annotations tools

  27. Development • Identification and comparison tools • form of annotation • flexibility in the ontology load • AutôMeta and GATE tools were selected • AutôMeta uses RDFa (Resource Description Framework in attributes) format • GATE is more sensitive for NLP tasks

  28. Ontology Community View an ontology or subset of an ontology Ontodog: A Web-based Ontology Community View Generator ontology annotation layer indicate the terms are in a specific community view imports community indication annotation layer annotation layer provide community specific annotations (e.g., user preferred label) of the terms imports community specific annotation layer Jie Zheng1*, Zuoshuang Xiang2*, Christian J. Stoeckert Jr1, Yongqun He2 1Department of Genetics, University of Pennsylvania, Philadelphia, PA 19104, USA 2 Department of Microbiology and Immunology, University of Michigan, Ann Arbor, MI 48109, USA * Authors contribute equivalently • a subset of the whole ontology • or tagged subset of terms in the whole ontology • user specified annotations (e.g., user preferred label)

  29. Examples of Community Views OBI FGED view: a simplified set of OBI terms with FGED community friendly labels OBI core: a view contains all OBI core terms with labels in different languages OBI (2011 Winter Release) 3501 terms -> 2279 terms in FGED view OBI (2012 Summer Release) 3691 terms -> around 100 terms in CORE view

  30. Web Interface Web Server Database Server Input term file (Tab-delimited or Excel file) PHP SPARQL queries RDF triple store of source ontologies Raw OWL output Annotation property setting Create annotation properties, Reformat raw OWL output Validate or Retrieve ontology terms OWL-API Community view OWL files for download Ontodog OWL outputs Ontodog System Architecture SPARQL related term retrieval approach is used for ontology subset extraction.

  31. Ontorat Web Server for Automatic Ontology Term Generation and Annotations Unit for Lab Animal Medicine, Department of Microbiology and Immunology, and Center for Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI 48109, USA • Needs: Often we need to (1) create a large number of new ontology terms or (2) annotate existing terms, that follow the same design patterns of logical definitions and axioms. • Challenge: Manual addition of these terms is time consuming, error prone, and often boring. • Goal: Ontorat is developed to facilitate this process. Zuoshuang “Allen” Xiang, Yu “Asiyah” Lin, Yongqun “Oliver” He

  32. Ontorat Design and Implementation Ontorat Web Interface: • Development Strategy: • Developed based on Ontology Design Patterns • Inspired by OBI Quick Term Templates (QTT) • Implement OBI QTT procedure • No RDF store used • Web-based user interface • Needs Three Parts for Execution: • Target ontology  to avoid ID duplication • Input data file: Excel or tab-delimited text form • Setting scripts  use Manchester OWL Syntax • (note: settings can be stored and reused) • Output Results: • OWL or Manchester syntax output file • Can be imported/merged to target ontology using Protege-OWL editor http://ontorat.hegroup.org

  33. Use case study • Use Ontorat to generate new licensed vaccines in Vaccine Ontology (VO) • Automatically created new VO terms for ~800 licensed vaccines • Advantages: • quick, user-friendly, • scalable, robust, • save/reuse templates. (A) Specify target ontology (B) Specify input data file (in Excel or tab-delimited text) (C) Generate settings using Manchester syntax (D) Execute and retrieve output OWL file (E) OWL output displayed and merged to target ontology using Protege.

  34. Thank You

More Related