Semantics for eScience Susie Stephens, Principal Research Scientist, Eli Lilly

1. Semantics for eScienceSusie Stephens, Principal Research Scientist, Eli Lilly

2. Outline • Introduction to the Semantic Web • W3C’s Semantic Web for Health Care and Life Sciences Interest Group • Semantic Web Solutions at Lilly

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 so need to be able to better share data • Increasing need in pharma to be able to query across data silos • 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 Applications Graph representation Data in various formats Source: Ivan Herman

7. Mashing Data Source: W3C

8. W3C’s Semantic Web for Health Care and Life Sciences Interest Group

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

10. BioRDF: Integrating Heterogeneous Data • Integration and analysis of heterogeneous data sets • Hypothesis, Genome, Pathways, Molecular Properties, Disease, etc. PDSPki NeuronDB Reactome Gene Ontology BAMS Allen Brain Atlas BrainPharm Antibodies Entrez Gene MESH NC Annotations PubChem Mammalian Phenotype SWAN AlzGene Homologene Publications

11. BioRDF: Looking for Targets for Alzheimer’s • Signal transduction pathways are considered to be rich in “druggable” targets • CA1 Pyramidal Neurons are known to be particularly damaged in Alzheimer’s disease • Casting a wide net, can we find candidate genes known to be involved in signal transduction and active in Pyramidal Neurons? Source: Alan Ruttenberg

12. BioRDF: SPARQL Query Source: Alan Ruttenberg

13. 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 Many of the genes are related to AD through gamma secretase (presenilin) activity Source: Alan Ruttenberg

14. LODD: Introduction Linked Data Browsers Linked DataMashups Search Engines Thing Thing Thing Thing Thing Thing Thing Thing Thing Thing typedlinks typedlinks typedlinks typedlinks A E C D B • Use Semantic Web technologies to • 1. publish structured data on the Web • 2. set links between data from one data source to data within other data sources Source: Chris Bizer

15. LODD: Potential Links between Data Sets Source: Chris Bizer

16. 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? Source: Chris Bizer

17. LODD: Linked Version of ClinicalTrials.gov 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) Bio2RDF.org's PubMed (from references) Source: Chris Bizer

18. Semantic Web Solutions at Lilly

19. Implementations at Lilly • Integration of Clinical and Pathways Data • Competitive Intelligence • Experimental Metadata • Discovery Metadata

20. 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 Source: Phil Brooks

21. SAP Legacy REFDB GSM NCBI Manual Curation Discovery Metadata: Ontology Source: Phil Brooks

22. A P P S Application 1 Application 2 Application 3 … S O A SOA Layer/Enterprise Service Bus (WebServices, Visualizers, DataAccess Components) Authentication D A T A SQL SPARQL ETL Provenance Source Model 1 Source Model 2 Source Model 3 Source Model 4 Local Assertions Top Level Ontology Other Sources Other Sources Source … Other Tools Spreadsheets Rdbms Discovery Metadata: Architecture Source: Phil Brooks

23. 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 • W3C’s Health Care and Life Sciences Interest Group

24. Conclusion • Many Semantic Web solutions are being explored within the health care and life sciences community • Lilly is seeing tangible benefits in multiple projects from Semantic Web • Semantic Web provides a flexible framework for data integration • Incremental adoption of technology • Flexibility to integrate unanticipated data sets • Link existing silos together • Lilly is setting up open collaborations in this space • Try out LSG