Phase 1: Bench to Bedside
The HCLS Knowledge Base (query interface) connects cell micro-anatomy and function, neuroanatomy and neuro-imaging data to publications and their associated MeSH terms. This community-supported KB supports phase 1 translational research by providing scientists with structured data reflecting current understanding of gene products. The related note, Experiences with the Conversion of SenseLab Databases to RDF/OWL, demonstrates how the HCLS KB can be extended with public or proprietary data by other institutions. Because the scale and distribution of the knowledge required for translational research exceeds makes data warehousing impractical, A journey to Semantic Web query federation in the life sciences (demo) and Provenance of Microarray Experiments for a Better Understanding of Experiment Results (Demo) show how to use SPARQL to federate queries across remote 7TM (7 Translational Membrane) and microarray databases respectively. This use case and the related SWObjects code base lead to the incorporation of SPARQL 1.1 Federation Extensions into the SPARQL 1.1 standard.
Changes in the state of the scientific art requires translational researchers to track evolving theories and interpretations of experimental results. Four notes:
- Semantic Web Applications in Neuromedicine (SWAN) Ontology - W3C Note 2009
- SIOC, SIOC Types and Health Care and Life Sciences - W3C Note 2009
- SWAN/SIOC: Alignment Between the SWAN and SIOC Ontologies - W3C Note 2009
- Ontology of Rhetorical Blocks (ORB) W3C Note 2011
provide structures for associating scientific assertions with meta-data such as media in which they are published. This helps phase 1 translational researchers efficiently locate and reason over data related to their research topic.
Phase 2: Clinical Observations
Health-intelligence requires access to patient data, either through conventional clinical data systems or through opt-in PCHRs like Indivo, Google Health, Microsoft Heath Vault, patientslikeme and 23 and Me. The Clinical Observations Interoperability Demonstration reasons over a de-identified hospital database and a human disease ontology to select cohorts based on user-specified inclusion and exclusion criteria. This demonstrator highlights the efficiency of Semantic Web query federation and integration with existing relational clinical systems.
The Translational Medicine Ontology integrates health care data with genomics for research and diagnostic support. TMO-Indivo demonstrates the use of GRDDL to express Indivo PCHRs as TMO. Many TM use cases require a information about compounds, their effects and their clinical history. To that end, the Linked Open Drug Data task force curated several public drug databases (winning the 2009 iTriplification Challenge) to support the motivating TM queries. Note these other papers about TMO and LODD:
- The Translational Medicine Ontology and Knowledge Base: driving personalized medicine by bridging the gap between bench and bedside
- Enabling Tailored Therapeutics with Linked Data
- Linked open drug data for pharmaceutical research and development
- Linked Data for Connecting Traditional Chinese Medicine and Western Medicine - Abstract for poster at DILS 2009
Phase 3: Clinical and Pharmacological Reasoning
While some of the motivating TM queries could be used in diagnosis, the HCLS IG will devote more work in the next charter to the areas of clinical decision support.
The HCLS IG has adopted, extended and created valuable vocabularies and data. With an eye towards non-proliferation of ontologies and interoperability with other standards, the group has used a representations of structured data which can be extended by others in or outside of the HCLS community. Community vetting of ontologies and tutorials for the query federation infrastructure have allowed helped disseminate the products of this IG to interested communities.