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

Outline Introduction to the Semantic Web W3Cs Semantic Web for Health Care and Life Sciences Interest Group Semantic Web Solutions at Lilly

Introduction to the Semantic Web

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

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

Creating a Web of Data Source: Ivan Herman Graph representation Data in various formats Applications

Mashing Data Source: W3C

W3Cs Semantic Web for Health Care and Life Sciences Interest Group

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

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

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? Source: Alan Ruttenberg

BioRDF: SPARQL Query Source: Alan Ruttenberg

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

LODD: Introduction B C Thing typed links A D E Thing Search Engines Linked Data Mashups Linked Data Browsers 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

LODD: Potential Links between Data Sets Source: Chris Bizer

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

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

Semantic Web Solutions at Lilly

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

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

Discovery Metadata: Ontology Source: Phil Brooks

Discovery Metadata: Architecture Application 1Application 2Application 3 … SOA Layer/Enterprise Service Bus (WebServices, Visualizers, DataAccess Components ) Authentication SOASOA DATADATA APPSAPPS SQLSPARQL Source Model 1 Source Model 2 Source Model 3 Source Model 4 Local Assertions Top Level Ontology Provenance Other Sources Other Sources Source … ETL Other Tools Spreadsheets Rdbms Source: Phil Brooks

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

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