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The Integration of Biological Data Using Semantic Web Technologies

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    1. The Integration of Biological Data Using Semantic Web Technologies

    3. Introduction to the Semantic Web

    4. Drivers for the Semantic Web Business models develop rapidly these days, so infrastructure that supports change is needed Organizations are increasingly forming and disbanding collaborations Data is growing so quickly that it is no longer possible for individuals to identify patterns in their heads Increasing recognition of the benefits of collective intelligence

    5. Characterizing the Semantic Web Semantic Web is an interoperability technology An architecture for interconnected communities and vocabularies A set of interoperable standards for knowledge exchange

    6. Creating a Web of Data

    7. Resource Description Framework (RDF)

    8. RDFS and OWL RDFS Is a simple vocabulary for describing properties and classes of RDF resources Provides semantics for hierarchies of properties and classes Designed to support inferencing OWL Explicitly represents meaning of terms in vocabularies and the relationships between those terms Separate layers have been defined balancing expressibility vs. implementability (OWL Lite, OWL DL, OWL Full) Supports inferencing

    9. SPARQL as a Unifying Source

    10. Semantic Web Solutions at Lilly

    11. Discovery Metadata: Goals Integrate master data throughout the discovery process to enable information sharing/integration for the scientific community Model key relationships between master data classes Provide ability to integrate disparate data sets quicker than the normal warehouse paradigm typically allows Create a re-usable and sustainable semantic implementation Allow for user-driven, manual curation of key data relationships

    12. Discovery Metadata: Ontology

    13. Discovery Metadata: Architecture

    14. CATIE: Overview Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) Was the most comprehensive independent trial ever completed to examine existing anti-psychotic therapies for schizophrenia Provides detailed information comparing the effectiveness and side effects of five medications currently used to treat schizophrenia Greatly enhances the knowledge available to guide treatment choices for people with schizophrenia

    15. CATIE: Goals Determine whether semantic integration and analysis of the CATIE data set in the context of metabolic and signal transduction pathways with receptor affinities can provide answers to specific scientific questions: Which pathways are associated with response to the 5 different schizophrenia drugs? How do these pathways compare between treatment arms? Which receptors are associated with response to the 5 schizophrenia drugs? How are the pathways, receptors and the drug response genes from the CATIE data set related?

    16. CATIE: Drugs and Data Sets CATIE Drugs: Olanzapine Perphenazine Quetiapine Risperidone Ziprasidone Datasets: Entrez Gene Pubchem Assay (Receptor Affinity Data) KEGG Reactome Biocyc Transpath

    17. CATIE: Architecture

    18. CATIE: Conclusions Efficient semantic integration can be accomplished by using RDF Powerful complex data modeling can be achieved by using graph principles inherent in RDF Easy translation of scientific questions to graph queries using SPARQL and SEM_MATCH Customized outputs can easily be generated by making slight changes in the SPARQL query pattern

    19. Competitive Intelligence: Overview Competitive Intelligence is a purposeful, ethical and co-coordinated monitoring of the competitors in any industry within a specific market place to: Strategically gain foreknowledge of recent developments of your competitor's plans Make calculated informed business decisions and formulate operational strategy Provide a mechanism for actively surveying the public information for competitive intelligence in Endocrine

    20. Competitive Intelligence: Goals Does such a CI effort significantly benefit from a semantic component? Does the project significantly benefit from semantic integration? Are there pre-existing ontologies for company and method of action domains? Does NLP or text mining work for this kind of data? Does buried knowledge exist within datasets that can be discovered using inference and reasoning?

    21. Competitive Intelligence: Integration Challenges

    22. Competitive Intelligence: NLP

    24. Competitive Intelligence: Inferencing

    25. Competitive Intelligence: Conclusions Semantic Integration (instance mapping using NLP) coupled with RDF data model was successful in answering questions in Competitive Intelligence Ontologies provide a powerful framework in providing dictionaries and taxonomical relations that help to reason and inference the data for knowledge discovery Manual curation is a tedious, error prone and labor intensive-task A semi-automated computer-based solution that utilizes ontologies, semantic integration and NLP could drastically reduce manual curation process and maintain high quality information

    26. Metadata Repository: Goals Aggregate experiment metadata from diverse relational databases into an Oracle 11g for scientific investigation Provide a unified vocabulary for scientific investigation Avoid a complex architecture and extended development effort Realize benefits in the near-term Preprocess metadata to improve efficiency Characterize the type of questions that ontology should answer Identify stable semantic technologies, do not employ parsers Allow semantic and relational databases to work together Provide browser, visualization, and query access into repository

    27. Metadata Repository: Ontology

    28. Metadata Repository: Architecture

    29. Metadata Repository: Implementation Protg Ontology Editor Oracle Semantic Technologies 11g D2R Map (Database to RDF Mapping) C# development in Visual Studio 2005 Current data sources include: Expression Data : Affymetrix, Illumina, Agilent aCGH Data RNAi Screening Data Reagent Data Gene Ontology (GO) Medical Subject Headings (MeSH) Currently ~30 million triples

    30. Metadata Repository: Conclusion Its now possible for users to ask questions such as: Get all the interactions for methylases that are involved in Colon cancer. For all these genes, get the expression and aCGH values for all LSCDD colon cancer samples Find cell lines in which RNAi data has been generated using Dharmacon reagents Retrieve the antibodies that have been used to assess the AKT1 pathway activity in MCF7 Find all the experiments that were done using my sample Find all samples which are grade III colorectal cancer. For these sample, retrieve the expression, mutation and aCGH data

    31. External Collaborations RDF Access to Relational Databases - Chris Bizer, Eric Prud'hommeaux Scalability testing of relational to RDF mapping approaches End User Semantic Web Authoring - David Karger Enhancing the scalability and robustness of the Exhibit and Potluck tools Scientist-Driven Semantic Integration of Knowledge in Alzheimer's Disease - Tim Clark, June Kinoshita Project to develop an integrated knowledge infrastructure for the neuromedical research community, pairing rich digital semantic context with the ever-growing digital scientific content on the web Provenance Collection and Management - Carole Goble, Beth Plale Project to develop a metadata taxonomy for global data at Lilly which enables the rapid integration of data and mining/analysis algorithms into dataflows which support clinical and discovery decisions W3Cs Health Care and Life Sciences Interest Group

    32. Conclusion Semantic Web provides a flexible framework for data integration Data integration needs (and issues) abound at Lilly Lilly is seeing tangible benefits in multiple projects from semantic Web Focus on incremental adoption of the technology Tools are improving, but more work is needed Lilly use of Semantic Web technology isnt atypical in health care and life sciences organizations

    33. W3C Semantic Web for Health Care and Life Sciences Interest Group

    34. What is the Mission of HCLS IG? The mission of HCLS is to develop, advocate for, and support the use of Semantic Web technologies for biological science, translational medicine and health care. These domains stand to gain tremendous benefit by adoption of Semantic Web technologies, as they depend on the interoperability of information from many domains and processes for efficient decision support.

    35. Task Forces Terminology Semantic Web representation of existing resources Task lead - John Madden BioRDF integrated neuroscience knowledge base Task lead - Kei Cheung Linking Open Drug Data aggregation of Web-based drug data Task lead - Chris Bizer Scientific Discourse building communities through networking Task leads - Tim Clark, John Breslin Clinical Observations Interoperability patient recruitment in trials Task lead - Vipul Kashyap Other Projects: Clinical Decision Support, URI Workshop, Collaborations with CDISC & HL7

    36. Terminology Task Force Task Lead: John Madden Participants: Chimezie Ogbuji, Helen Chen, Holger Stenzhorn, Mary Kennedy, Xiashu Wang, Rob Frost, Jonathan Borden, Guoqian Jiang

    37. Terminology: Overview Goal is to identify use cases and methods for extracting Semantic Web representations from existing, standard medical record terminologies, e.g. UMLS Methods should be reproducible and, to the extent possible, not lossy Identify and document issues along the way related to identification schemes, expressiveness of the relevant languages Initial effort will start with SNOMED-CT and UMLS Semantic Networks and focus on a particular sub-domain (e.g. pharmacological classification)

    38. BioRDF Task Force Task Lead: Kei Cheung Participants: Scott Marshall, Eric Prudhommeaux, Susie Stephens, Andrew Su, Steven Larson, Huajun Chen, TN Bhat, Matthias Samwald, Erick Antezana, Rob Frost, Ward Blonde, Holger Stenzhorn, Don Doherty

    39. BioRDF: Answering Questions Goals: Get answers to questions posed to a body of collective knowledge in an effective way Knowledge used: Publicly available databases, and text mining Strategy: Integrate knowledge using careful modeling, exploiting Semantic Web standards and technologies

    40. BioRDF: Looking for Targets for Alzheimers Signal transduction pathways are considered to be rich in druggable targets CA1 Pyramidal Neurons are known to be particularly damaged in Alzheimers disease Casting a wide net, can we find candidate genes known to be involved in signal transduction and active in Pyramidal Neurons?

    42. BioRDF: SPARQL Query

    43. BioRDF: Results: Genes, Processes DRD1, 1812 adenylate cyclase activation ADRB2, 154 adenylate cyclase activation ADRB2, 154 arrestin mediated desensitization of G-protein coupled receptor protein signaling pathway DRD1IP, 50632 dopamine receptor signaling pathway DRD1, 1812 dopamine receptor, adenylate cyclase activating pathway DRD2, 1813 dopamine receptor, adenylate cyclase inhibiting pathway GRM7, 2917 G-protein coupled receptor protein signaling pathway GNG3, 2785 G-protein coupled receptor protein signaling pathway GNG12, 55970 G-protein coupled receptor protein signaling pathway DRD2, 1813 G-protein coupled receptor protein signaling pathway ADRB2, 154 G-protein coupled receptor protein signaling pathway CALM3, 808 G-protein coupled receptor protein signaling pathway HTR2A, 3356 G-protein coupled receptor protein signaling pathway DRD1, 1812 G-protein signaling, coupled to cyclic nucleotide second messenger SSTR5, 6755 G-protein signaling, coupled to cyclic nucleotide second messenger MTNR1A, 4543 G-protein signaling, coupled to cyclic nucleotide second messenger CNR2, 1269 G-protein signaling, coupled to cyclic nucleotide second messenger HTR6, 3362 G-protein signaling, coupled to cyclic nucleotide second messenger GRIK2, 2898 glutamate signaling pathway GRIN1, 2902 glutamate signaling pathway GRIN2A, 2903 glutamate signaling pathway GRIN2B, 2904 glutamate signaling pathway ADAM10, 102 integrin-mediated signaling pathway GRM7, 2917 negative regulation of adenylate cyclase activity LRP1, 4035 negative regulation of Wnt receptor signaling pathway ADAM10, 102 Notch receptor processing ASCL1, 429 Notch signaling pathway HTR2A, 3356 serotonin receptor signaling pathway ADRB2, 154 transmembrane receptor protein tyrosine kinase activation (dimerization) PTPRG, 5793 ransmembrane receptor protein tyrosine kinase signaling pathway EPHA4, 2043 transmembrane receptor protein tyrosine kinase signaling pathway NRTN, 4902 transmembrane receptor protein tyrosine kinase signaling pathway CTNND1, 1500 Wnt receptor signaling pathway

    44. LODD Task Force Task Lead: Chris Bizer Participants: Anja Jentzsch, Kristin Tolle, Eric Prudhommeaux, Don Doherty, Susie Stephens, Bosse Andersson, Scott Marshall, Glen Newton, Michel Dumontier, TN Bhat, Oktie Hassanzadeh

    45. LODD: Introduction

    46. LODD: Potential Links between Data Sets

    47. LODD: Data Set Evaluation

    48. LODD: Potential questions to answer Physicians and Pharmacists What are alternative drugs for a given indication (disease)? What are equivalent drugs (generic version of a brand name, or the chemical name of a active ingredient)? Are there ongoing clinical trials for a drug? Patients What background information is available about a drug? What are the contraindications of a drug? Which alternative drugs are available? What are the results of clinical trials for a drug? Pharmaceutical Companies What are other companies with drugs in similar areas? Which companies have a similar therapeutic focus?

    49. LODD: Linked Version of Total number of triples: 6,998,851 Number of Trials: 61,920 RDF links to other data sources: 177,975 Links to: DBpedia and YAGO (from intervention and conditions) GeoNames (from locations)'s PubMed (from references)

    50. LODD: Mashing Clinical Trials and Geo

    51. Scientific Discourse Task Force Task Lead: Tim Clark, John Breslin Participants: Uldis Bojars, Paolo Ciccarese, Sudeshna Das, Ronan Fox, Tudor Groza, Christoph Lange, Matthias Samwald, Elizabeth Wu, Holger Stenzhorn, Marco Ocana, Kei Cheung, Alexandre Passant

    52. Scientific Discourse: Overview

    53. Scientific Discourse: Goals Provide a Semantic Web platform for scientific discourse in biomedicine Linked to key concepts, entities and knowledge Specified by ontologies Integrated with existing software tools Useful to Web communities of working scientists

    54. Scientific Discourse: Some Parameters Discourse categories: research questions, scientific assertions or claims, hypotheses, comments and discussion, and evidence Biomedical categories: genes, proteins, antibodies, animal models, laboratory protocols, biological processes, reagents, disease classifications, user-generated tags, and bibliographic references Driving biological project: cross-application of discoveries, methods and reagents in stem cell, Alzheimer and Parkinson disease research Informatics use cases: interoperability of web-based research communities with (a) each other (b) key biomedical ontologies (c) algorithms for bibliographic annotation and text mining (d) key resources

    55. Scientific Discourse: SWAN+SIOC SIOC Represent activities and contributions of online communities Integration with blogging, wiki and CMS software Use of existing ontologies, e.g. FOAF, SKOS, DC SWAN Represents scientific discourse (hypotheses, claims, evidence, concepts, entities, citations) Used to create the SWAN Alzheimer knowledge base Active beta participation of 144 Alzheimer researchers Ongoing integration into SCF Drupal toolkit

    56. COI Task Force Task Lead: Vipul Kashap Participants: Eric Prudhommeaux, Helen Chen, Jyotishman Pathak, Rachel Richesson, Holger Stenzhorn

    57. COI: Bridging Bench to Bedside How can existing Electronic Health Records (EHR) formats be reused for patient recruitment? Quasi standard formats for clinical data: HL7/RIM/DCM healthcare delivery systems CDISC/SDTM clinical trial systems How can we map across these formats? Can we ask questions in one format when the data is represented in another format?

    58. COI: Use Case Pharmaceutical companies pay a lot to test drugs Pharmaceutical companies express protocol in CDISC -- precipitous gap Hospitals exchange information in HL7/RIM Hospitals have relational databases

    59. Type 2 diabetes on diet and exercise therapy or monotherapy with metformin, insulin secretagogue, or alpha-glucosidase inhibitors, or a low-dose combination of these at 50% maximal dose. Dosing is stable for 8 weeks prior to randomization. ?patient takes meformin . Inclusion Criteria

    60. Use of warfarin (Coumadin), clopidogrel (Plavix) or other anticoagulants. ?patient doesNotTake anticoagulant . Exclusion Criteria

    61. ?medication1 sdtm:subject ?patient ; spl:activeIngredient ?ingredient1 . ?ingredient1 spl:classCode 6809 . #metformin OPTIONAL { ?medication2 sdtm:subject ?patient ; spl:activeIngredient ?ingredient2 . ?ingredient2 spl:classCode 11289 . #anticoagulant } FILTER (!BOUND(?medication2)) Criteria in SPARQL

    62. Getting Involved Benefits to getting involved include: early access to use cases and best practice influence standard recommends cost effective exploration of new technology through collaboration Get involved by contacting the chairs: