1. 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.

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

3. 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:

4. 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

5. 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 million by 2050
Many different types of evidence need to be integrated
An active Web community exists for AD research

6. 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

7. 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?

8. 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

9. 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

10. 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

11. 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