Developing a Kidney and Urinary Pathway Knowledge Base
Author Information
Author(s): Simon Jupp, Julie Klein, Joost Schanstra, Robert Stevens
Primary Institution: University of Manchester, UK
Hypothesis
The integration of experimental results from multiple biological levels can facilitate early detection and diagnosis of chronic renal disease.
Conclusion
The KUPKB allows biologists to aggregate knowledge necessary for answering biological questions related to kidney and urinary pathways.
Supporting Evidence
- The KUPKB integrates experimental findings with background knowledge.
- Semantic Web technologies enable rapid conversion and integration of the knowledge base.
- The KUPKB can be queried over the Web via a SPARQL endpoint.
Takeaway
This study created a special database to help scientists understand kidney diseases better by connecting different types of biological data.
Methodology
A Semantic Web approach was used to develop a knowledge base integrating data from high-throughput experiments on kidney and urine.
Limitations
The KUPKB is still relatively small, raising questions about scalability, maintenance, and availability of the knowledge.
Digital Object Identifier (DOI)
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