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

2. Background of the HCLS IG
Originally chartered in 2005
Chairs: Eric Neumann and Tonya Hongsermeier
Re-chartered in 2008
Chairs: Scott Marshall and Susie Stephens
Team contact: Eric Prud’hommeaux
101 formal participants, and mailing list of > 600
Information about the group

3. 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
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

4. Group Activities
Document use cases to aid individuals in understanding the business and technical benefits of using Semantic Web technologies
Document guidelines to accelerate the adoption of the technology
Implement a selection of the use cases as proof-of-concept demonstrations
Develop high-level vocabularies
Disseminate information about the group’s work at government, industry, and academic events

5. Task Forces BioRDF – integrated neuroscience knowledge base
Kei Cheung (Yale University)
Clinical Observations Interoperability – patient recruitment in trials
Vipul Kashyap (Cigna Healthcare)
Linking Open Drug Data – aggregation of Web-based drug data
Chris Bizer (Free University Berlin)
Pharma Ontology – high level patient-centric ontology
Christi Denney (Eli Lilly)
Scientific Discourse – building communities through networking
Tim Clark (Harvard University)
Terminology – Semantic Web representation of existing resources
John Madden (Duke University)

6. 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
Participants: Kei Cheung, Scott Marshall, Eric Prud’hommeaux, Susie Stephens, Andrew Su, Steven Larson, Huajun Chen, TN Bhat, Matthias Samwald, Erick Antezana, Rob Frost, Ward Blonde, Holger Stenzhorn, Don Doherty

7. 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?

8. BioRDF: Integrating Heterogeneous Data
PDSPki
Gene Ontology
Reactome
NeuronDB
BAMS
Allen Brain Atlas
Antibodies
BrainPharm
Entrez Gene
MESH
Literature
PubChem
Mammalian Phenotype
SWAN
AlzGene
Homologene

9. BioRDF: SPARQL Query

10. BioRDF: Results: Genes, Processes
DRD1, adenylate cyclase activation
ADRB2, 154 adenylate cyclase activation
ADRB2, 154 arrestin mediated desensitization of G-protein coupled receptor protein signaling pathway
DRD1IP, dopamine receptor signaling pathway
DRD1, dopamine receptor, adenylate cyclase activating pathway
DRD2, dopamine receptor, adenylate cyclase inhibiting pathway
GRM7, G-protein coupled receptor protein signaling pathway
GNG3, G-protein coupled receptor protein signaling pathway
GNG12, G-protein coupled receptor protein signaling pathway
DRD2, 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, G-protein coupled receptor protein signaling pathway
DRD1, G-protein signaling, coupled to cyclic nucleotide second messenger
SSTR5, G-protein signaling, coupled to cyclic nucleotide second messenger
MTNR1A, G-protein signaling, coupled to cyclic nucleotide second messenger
CNR2, G-protein signaling, coupled to cyclic nucleotide second messenger
HTR6, G-protein signaling, coupled to cyclic nucleotide second messenger
GRIK2, glutamate signaling pathway
GRIN1, glutamate signaling pathway
GRIN2A, glutamate signaling pathway
GRIN2B, glutamate signaling pathway
ADAM10, 102 integrin-mediated signaling pathway
GRM7, negative regulation of adenylate cyclase activity
LRP1, negative regulation of Wnt receptor signaling pathway
ADAM10, 102 Notch receptor processing
ASCL1, 429 Notch signaling pathway
HTR2A, serotonin receptor signaling pathway
ADRB2, 154 transmembrane receptor protein tyrosine kinase activation (dimerization)
PTPRG, ransmembrane receptor protein tyrosine kinase signaling pathway
EPHA4, transmembrane receptor protein tyrosine kinase signaling pathway
NRTN, transmembrane receptor protein tyrosine kinase signaling pathway
CTNND1, Wnt receptor signaling pathway
Many of the genes are related to AD through gamma secretase (presenilin) activity

11. Linking Open Drug Data HCLSIG task started October 1st, 2008
Primary Objectives
Survey publicly available data sets about drugs
Explore interesting questions from pharma, physicians and patients that could be answered with Linked Data
Publish and interlink these data sets on the Web
Participants: Bosse Andersson, Chris Bizer, Kei Cheung, Don Doherty, Oktie Hassanzadeh, Anja Jentzsch, Scott Marshall, Eric Prud’hommeaux, Matthias Samwald, Susie Stephens, Jun Zhao

12. Linked Data
Use Semantic Web technologies to publish structured data on the Web and set links between data from one data source and data from another data sources B C
Thing
typed links A D E
Search Engines
Linked Data Mashups
Linked Data
Browsers

13. Dereferencing URIs over the Web
rdf:type
pd:cygri
foaf:Person
foaf:name
dp:Cities_in_Germany
dp:population
skos:subject
Richard Cyganiak
foaf:based_near
dbpedia:Berlin
skos:subject
dbpedia:Hamburg
skos:subject
dbpedia:Meunchen

14. LODD Data Sets

15. The Linked Data Cloud

16. Translational Medicine Ontology

17. Deliverables Review existing ontology landscape
Identify scope of a translational medicine ontology through understanding employee roles
Identify roughly 40 entities and relationships for template ontology
Create 2-3 sketches of use cases (that cover multiple roles)
Select and build out use case (including references to data sets)
Build extensions to the ontology to meet the use case
Build an application that utilizes the ontology

18. Roles within Translational Medicine

19. Translational Medicine Use Cases

20. Translational Medicine Ontology

21. 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

22. Scientific Discourse: Overview

23. 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

24. 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

25. Scientific Discourse: SWAN+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

26. Scientific Discourse Workshop

27. COI Task Force Task Lead: Vipul Kashap
Participants: Eric Prud’hommeaux, Helen Chen, Jyotishman Pathak, Rachel Richesson, Holger Stenzhorn

28. 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?

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

30. 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)

31. Accomplishments Technical Outreach
HCLS KB hosted at 2 institutes, with content from over 20 data sources
Added many data sources to the Linked Data Cloud
Integration of SWAN and SIOC ontologies for Scientific Discourse
Demonstrator of querying inclusion/exclusion criterion across heterogeneous EHR systems
Outreach
Conference Presentations and Workshops:
Bio-IT World, WWW, ISMB, ISWC, AMIA, Society for Neuroscience, C-SHALS, etc.
Publications:
iTriplification Challenge: Linking Open Drug Data
DILS: Linked Data for Connecting Traditional Chinese Medicine and Western Medicine
ICBO: Pharma Ontology: Creating a Patient-Centric Ontology for Translational Medicine
LOD Workshop, WWW: Enabling Tailored Therapeutics with Linked Data
AMIA Spring Symposium: Clinical Observations Interoperability: A Semantic Web Approach
W3C Note: Semantic Web Applications in Neuromedicine (SWAN) Ontology
W3C Note: SIOC, SIOC Types and Health care and Life Sciences
W3C Note: Alignment Between the SWAN and SIOC Ontologies
W3C Note: A Prototype Knowledge Base for the Life Sciences
W3C Note: Experiences with the Conversion of SenseLab Databases to RDF/OWL
BMC Bioinformatics: Advanced Translational Research with the Semantic Web

32. Conclusions Early access to use cases and best practice
Influence standard recommendations
Cost effective exploration of new technology through collaboration
Network with others working on the Semantic Web
Group generates resources ranging from papers, use cases, demos, ontologies, and data