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  • IE status applications: Sivaram Arabandi & Claus Stie Kallesøe
  • Healthcare Compliance issues

Translational Medicine Strategy


 * Translation of preclinical science into patient studies
 * Use of clinical data to humanize preclinical drug discovery @@what's that? -- EGP
 * Essential for personalized medicine
 * Which requires identification of biomarkers
 * Critical to be able to integrate and analyze many sources of data
 * Data ranges from eHR, to genetic data, formulary data, side effects data, market data
 * A translational strategy should focus on 5 layers: Identifier, Data, Ontologies, Analysis, Visualization

Identifiers (URI's)

 * Requires recommendation as to how identifiers should be structured
 * No work currently ongoing within HCLS in this area
 * Are collaborating with Shared Names feeding requirements from BioRDF (Eric and Scott are members of SN Steering)
 * Types of identifiers needed: Gene id's, protein id's, PubMed ID's, SNP id's, People id's, Institution id's, Chemical Compound id's, ..


 * Diverse range of data needs to be made available through RDF (i.e. SPARQL)
 * Should work with data providers to make this happen
 * Devise best practices for mapping different data types to RDF
 * Make recommendations regarding anonymization


 * Polished ontology for translational medicine
 * Map translational medicine ontology to other ontologies


 * Demonstrate basic querying of a data set to gain insights of interest
 * Demonstrate federated queries
 * Demonstrate ability to aggregate data about entities of interest


 * Build demos that enable simple and federated queries
 * Demonstrate enhanced ability to gain insights with semantic web data


 * Output should consists of demos that lay people can comprehend
 * Best practices should be written up to help people to follow our approach
 * Our work should be promoted in HCLS literature, and at HCLS conferences



  • Objectives (end) - Specific, measurable, bounded by time - Ends against which actual performance will be evaluated
  • Advantage (means) - Should describe your company’s edge - What you do uniquely, differently, and better than competitors - The drivers of the source of that advantage
  • Scope (domain) - Who you serve, where, what you’ll do, what boundaries won’t you cross
  • To achieve broad adoption of a global information framework based on the Semantic Web (Objective)
  • by showing its ability to enhance insight and decision making through deep expertise in W3C standards and knowledge of best practices (Advantage)
  • in health care and life sciences on a global scale by offering RDF formatted data, useful ontologies, implementation guides, demos, and and strategic alliances with other standards organizations, and consortia (scope)

TODO for Strategy

  • Strategy Wheel
  • Analysis of competitvie advantage
  • Timeline
  • Strategy Map
  • Balanced Scorecard

  • Note: Unclear image of the interest group to prospective participants, its role and activities - "I have tried to follow HCLS activities a few times but can't figure it out. It's big.". Take steps to remove the mystery.

2010 Goals

  • More LODD data sets available
  • Promoting and enhancing the TMO
  • Creating a drug ontology for FipNet
  • Incorporate microarray use case into BioRDF demo
  • Support development of terminology servers that serve SKOS
  • Creating SPARQL endpoint for both Uniprot and NCBO
  • Demonstrate export of SWAN/SIOC RDF for a discussion forum (e.g. SWAN) or collaborative website (myExperiment)
  • Reach out to caBIG
  • Security & SPARQL
  • Vocabulary server

Front Page Blurb

Welcome to the W3C Semantic Web in Health Care and Life Sciences Interest Group (HCLS) home page. This group concearns itself with vocabularies, technologies and practices around translational medicine. The breadth of translational medicine demands integration of several technologies and data sources. The work in HCLS is divided into six task forces:

Group Description
Terminology Develop Semantic Web representations of existing resources.

We draw on existing terminologies to embrace and extend current practice for e.g. medical observations coded in SNOMED-CT.

Scientific Discourse Code hypotheses, experiments, assertions and publications.
BioRDF integrated neuroscience knowledge base
Clinical Observations Interoperability patient records, patient recruitment, clinical decision support
Linking Open Drug Data aggregation of Web-based drug data
Translational Medicine Ontology patient-centric upper level ontology

These task forces manage identification, analysis, visualization and domain ontologies to provide a wholistic and scalable approach to translational medicine.