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
Broad industry participation
Over 100 members
Mailing list of over 600
Background Information

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. Accomplishments Technical Outreach HCLS KB hosted at 2 institutes
Linked Open Data contributions
Demonstrator of querying across heterogeneous EHR systems
Integration of SWAN and SIOC ontologies for Scientific Discourse
Outreach
Conference Presentations and Workshops:
Bio-IT World, WWW, ISMB, AMIA, C-SHALS, etc.
Publications:
Proceedings of LOD Workshop at WWW 2009: Enabling Tailored Therapeutics with Linked Data
Proceedings of the ICBO: Pharma Ontology: Creating a Patient-Centric Ontology for Translational Medicine
AMIA Spring Symposium: Clinical Observations Interoperability: A Semantic Web Approach
BMC Bioinformatics. A Journey to Semantic Web Query Federation in Life Sciences

6. Current 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)

7. Getting Involved Get involved Benefits to getting involved include:
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
Get involved
Speak to any of us after the session!
chairs and team contact

8. Sponsored by the University of Southampton
W3C Semantic Web for Health Care and Life Sciences Interest Group
Sponsored by the University of Southampton

9. Sponsored by the University of Southampton
Agenda – Day One
Welcome
Semantic Web State of the Union in HCLS
Lessons Learned in Describing Neuroscience on the Road to Resource Discovery - Maryann Martone
Break
Task Overviews
Lunch (incl. presentation on Brain imaging by Carl Taswell)
Task Breakouts (BioRDF, Pharma Ontology, Scientific Discourse)
Task Breakout Reports
Wrap Up
R&D Pub
Dinner at Cambridge Brew Company
Sponsored by the University of Southampton

10. Sponsored by the University of Southampton
Agenda – Day Two
Welcome
myExperiment: towards Research Objects - David de Roure
Break
HCLS Strategy
Integration Points across Tasks
Conference/Paper Outreach Opportunities
Grant Opportunities
Lunch
Terminology Discussion
Wrap Up
Sponsored by the University of Southampton

11. State of the Union of the Semantic Web in Health Care and Life Sciences Susie Stephens, Principal Research Scientist, Eli Lilly

12. Agenda Trends in Health Care and Life Sciences Trends in Technology
Implication of Trends on Semantic Web

13. Trends in Health Care and Life Sciences
Genomics revolution
Tailored therapeutics
Business models
Electronic health records
Publication paradigm
Genomics revolution (sequencing, SNPs, HapMaps, epigenetics), 23&me, Navigenics, Knome – understanding in biology
Tailored therapeutics - Bridge discovery through to clinic – integrative biology – efficacy and safety – more holistic view that encompasses wellness and prevention
Shifting pharma business model - More competitive and pre-competitive collaborations – including outsourcing – biomedical communities
eHR – Obama – efficiency, research, PCMR vs classic vendor solutions
Shifting publication paradigm

14. Genomics Revolution
Cell/Assay
Technologies

15. Tailored Therapeutics
Target
Hit
Lead
PgS CS
FHD
FED
PD/RD FS FA FL GL To
Optimization
Pre-Clinical
Development
Phase I
Phase 2
Phase 3
Registration
Launch
Global
Project
Program
Product
Exploratory
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine
Data
Transform
Model &
Understand
Generate/Test
Hypothesis
Analyze
& Mine

16. Source: H. Chesbrough, Open Innovation
Business Models
Research
Development
New Market
Boundary of the firm
Research
Projects
Current Market
So the introduction to open innovation.
In the past a lot of significant innovations happened within the research centers of large companies.
Companies used to do well by developing everything themselves, e.g. bell labs part of lucent.
But now cisco can do just as well by making external investments and acquiring companies that do well.
These are 2 very different models for achieving innovation – the companies that don’t invest in research are doing as well as those that aren’t.
So what has changed?
External ideas and finance have become plentiful as more people have become educated, and VC funding has developed.
Individuals can leave companies and develop their project externally if the original parent company decided to not go ahead with it.
It’s hard for a company with a large research group to change direction quickly, or change skill sets quickly.
This figure is by Henry Chesbrough, one of the big names in OI. And he believes that the world is going through a paradigm shift in how companies commercialize industrial knowledge. Less need to control the research now – less need for closed innovation.
The figure shows that some research projects….
Source: H. Chesbrough, Open Innovation

17. Electronic Health Records

18. Publication Paradigm

19. Technology Trends Cloud computing Security, Identity SOA Web 2.0
Security and Identity
SOA
Web 2.0 / social networking / user contribution
Semantic Web / Linked Data

20. Cloud Computing

21. Security, Identity

22. SOA

23. Web 2.0

24. Impact on the Semantic Web?

25. Semantic Web: Strengths
Business
Trends all require more flexibility, and better integration and sharing of data
Technology
Complimentary technologies are gaining considerable traction
Key Semantic Web standards are mostly in place
Linked Data has raised awareness and understanding of the Semantic Web

26. Semantic Web: Weaknesses
Business
Limited awareness of Semantic Web
Prevailing organizational culture
Lack of Semantic Web experts
Technology
Limited interaction between technologies
Ontology alignment and large scale reasoning
Complexity of the technology
Lack of quality vendor solutions

27. Semantic Web: Opportunities
Business
Bridging divide between genomics and the clinic
Interacting with domain focused standards organizations
Technology
Federated query
Easy to use interfaces
Alignment with other technologies

28. Semantic Web: Threats Business Technology
The use of the phrase ‘Semantic Interoperability’ confuses people
US Government focus on CDISC/HL7
Complimentary technologies are gaining considerable traction

29. Conclusions
Many trends in HCLS are leading to increased interest in the Semantic Web
It’s important for us to interact with other groups and technologies
There are many opportunities for the HCLS IG