W3C Semantic Web for Health Care and Life Sciences Interest Group

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 http://www.w3.org/2001/sw/hcls/ http://esw.w3.org/topic/HCLSIG

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

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

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

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)

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! Email chairs and team contact team-hcls-chairs@w3.org

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

Sponsored by the University of Southampton Agenda – Day One 08.45-09.00 - Welcome 09.00-09.45 - Semantic Web State of the Union in HCLS 09.45-10.30 - Lessons Learned in Describing Neuroscience on the Road to Resource Discovery - Maryann Martone 10.30-11.00 - Break 11.00-12.30 - Task Overviews 12.30-13.30 - Lunch (incl. presentation on Brain imaging by Carl Taswell) 13.30-16.30 - Task Breakouts (BioRDF, Pharma Ontology, Scientific Discourse) 16.30-17.30 - Task Breakout Reports 17.30-18.00 - Wrap Up 18.00-19.30 - R&D Pub 19.30 - Dinner at Cambridge Brew Company Sponsored by the University of Southampton

Sponsored by the University of Southampton Agenda – Day Two 08.45-09.00 - Welcome 09.00-10.00 - myExperiment: towards Research Objects - David de Roure 10.00-10.30 - Break 10.30-11.00 - HCLS Strategy 11.00-11.30 - Integration Points across Tasks 11.30-12.00 - Conference/Paper Outreach Opportunities 12.00-12.30 - Grant Opportunities 12.30-13.30 - Lunch 13.30-15.30 - Terminology Discussion 15.30-16.00 - Wrap Up Sponsored by the University of Southampton

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

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

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

Genomics Revolution Cell/Assay Technologies

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

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

Electronic Health Records

Publication Paradigm

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

Cloud Computing

Security, Identity

SOA

Web 2.0

Impact on the Semantic Web?

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

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

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

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

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