Annotation of SBML models through rule-based semantic integration View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2010-06

AUTHORS

Allyson L Lister, Phillip Lord, Matthew Pocock, Anil Wipat

ABSTRACT

BACKGROUND: The creation of accurate quantitative Systems Biology Markup Language (SBML) models is a time-intensive, manual process often complicated by the many data sources and formats required to annotate even a small and well-scoped model. Ideally, the retrieval and integration of biological knowledge for model annotation should be performed quickly, precisely, and with a minimum of manual effort. RESULTS: Here we present rule-based mediation, a method of semantic data integration applied to systems biology model annotation. The heterogeneous data sources are first syntactically converted into ontologies, which are then aligned to a small domain ontology by applying a rule base. We demonstrate proof-of-principle of this application of rule-based mediation using off-the-shelf semantic web technology through two use cases for SBML model annotation. Existing tools and technology provide a framework around which the system is built, reducing development time and increasing usability. CONCLUSIONS: Integrating resources in this way accommodates multiple formats with different semantics, and provides richly-modelled biological knowledge suitable for annotation of SBML models. This initial work establishes the feasibility of rule-based mediation as part of an automated SBML model annotation system. AVAILABILITY: Detailed information on the project files as well as further information on and comparisons with similar projects is available from the project page at http://cisban-silico.cs.ncl.ac.uk/RBM/. More... »

PAGES

s3

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/2041-1480-1-s1-s3

DOI

http://dx.doi.org/10.1186/2041-1480-1-s1-s3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1023001150

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/20626923


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