Harnessing the Semantic Web to Answer Scientific Questions: A Health Care and Life Sciences Interest Group demo Susie Stephens, Principal Research Scientist, Lilly Let me introduce Steve Caltrider, the 6 Sigma blackbelt and team leader for this initiative. Steve’s day job is as assistant general patent counsel in the legal division.

Agenda Health Care and Life Sciences Interest Group Scientific Use Case Technological Approach Demonstration Benefits of the Semantic web

Health Care & Life Sciences Interest Group HCLSIG is chartered to develop and support the use of Semantic Web technologies and practices to improve collaboration, research and development, and innovation adoption in the Health Care and Life Science domains More details on HCLS are available at: http://www.w3.org/2001/sw/hcls/

Benefits of Semantic Web Technologies Fusion of data across many scientific disciplines Easier recombination of data Querying of data at different levels of granularity Capture provenance of data through annotation Perform inference across data sets Machine processable approach Data can be assessed for inconsistencies

Scientific Use Case Use case focuses on Alzheimer’s Disease AD is a devastating illness that impacts 26.6 million people worldwide Prevalence is predicted to quadruple to 106.8 million by 2050 Many different types of evidence need to be integrated An active Web community exists for AD research

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

Scientific Hypothesis & Research Questions Amyloid beta peptide may impair memory by inhibiting long-term potentiation (LTP) Research Questions: - By what mechanism does amyloid beta inhibit LTP? - Can we identify a novel therapeutic target based on this mechanism? - How can we validate the therapeutic target?

Technological Approach Careful modeling that reflect biology to enable integration of data sources All bio-entities were assigned URIs Most data translated to RDF and managed in a triple store Other data maintained in original store and mapped to RDF Using a reasoner to infer triples to increase expressiveness of queries Query data with SPARQL and visualization tools

Conclusions Semantic Web provides ability to query across many disparate data sources to discover new insights Potential to identify patterns and insights across many data sources Data needs to be carefully modeled Flexible re-use of data, which is important in a discipline where knowledge is frequently updated

Acknowledgements HCLS Demo Contributors HCLS Demo Contributors John Barkley (NIST) Olivier Bodenreider (NLM, NIH) Bill Bug (Drexel University College of Medicine) Huajun Chen (Zhejiang University) Paolo Ciccarese (SWAN) Kei Cheung (SenseLab, Yale) Tim Clark (SWAN) Don Doherty (Brainstage Research Inc.) Kerstin Forsberg (AstraZeneca) Ray Hookaway (HP) Vipul Kashyap (Partners Healthcare) June Kinoshita (AlzForum) Joanne Luciano (Harvard Medical School) Scott Marshall (University of Amsterdam) Chris Mungall (NCBO) Eric Neumann (Teranode) Eric Prud’hommeaux (W3C) Jonathan Rees (Science Commons) Alan Ruttenberg (Science Commons) Matthias Samwald (Medical University of Vienna) HCLS Demo Contributors Susie Stephens (Eli Lilly) Mike Travers ( Gwen Wong (SWAN) Elizabeth Wu (SWAN) Data Providers Judith Blake (MGD.) Mikail Bota (BAMS) David Hill (MGD) Oliver Hoffman (CL) Minna Lehvaslaiho (CL) Colin Knep (Alzforum) Maryanne Martone (CCDB) Susan McClatchy (MGD) Simon Twigger (RGD) Allen Brain Institute Vendor Support OpenLink - Kingsley Idehen, Ivan Mikhailov, Orri Erling, Mitko Iliev HP - Ray Hookaway, Jeannine Crockford

NeuronDB PDSPki GO Reactome BAMS BrainPharm Entrez Gene Protein (channels/receptors) Neurotransmitters Neuroanatomy Cell Compartments Currents Proteins Chemicals Neurotransmitters GO Reactome Genes/proteins Interactions Cellular location Processes (GO) Molecular function Cell components Biological process Annotation gene PubMedID BAMS BrainPharm Protein Neuroanatomy Cells Metabolites (channels) PubmedID Drug Drug effect Pathological agent Phenotype Receptors Channels Cell types pubMedID Disease Entrez Gene Allen Brain Atlas Antibodies Genes Brain images Gross anatomy -> neuroanatomy Genes Antibodies Genes Protein GO pubmedID Interaction (g/p) Chromosome C. location MESH Drugs Anatomy Phenotypes Compounds Chemicals PubMedID PubChem Genes/Proteins Processes Cells (maybe) PubMed ID Name Structure Properties Mesh term NC Annotations Genes Phenotypes Disease PubMedID PubChem Genes Species Orthologies Proofs Mammalian Phenotype PubMedID Hypothesis Questions Evidence Genes Gene Polymorphism Population Alz Diagnosis Homologene SWAN AlzGene