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Henkel R, Wolkenhauer O, Walthemath D.2014. Combining computational models, semantic annotations, and associated simulation experiments in a graph database. PeerJ PrePrints2:e376v1https://doi.org/10.7287/peerj.preprints.376v1
Model repositories such as the BioModels Database or the CellML Model Repository are frequently accessed to retrieve computational models describing biological systems. However, the current designs of these databases limit the types of supported queries, and many data in these repositories cannot easily be accessed. Computational methods for model retrieval cannot be applied. In this paper we present a storage concept that meets this challenge. It grounds on a graph database, reflects the models' structure, incorporates semantic annotations and experiment descriptions, and ultimately connects different types of model-related data.The connections between heterogeneous model-related data and bio-ontologies enable efficient search via biological facts and grant access to new model features such as network structure.The introduced concept notably improves the access of computational models and associated simulations in a model repository. This has positive effects on tasks such as model search, retrieval, ranking, matching, filtering etc.We exemplify how CellML- and SBML-encoded models can be maintained in one database, how these models can be linked via annotations, and queried.
How To: WebAPI and Queries described in this paper.
Described in this PDF is the access and usage of the WebAPI. All queries listed in the paper can be found here, ready for copy & paste.
I love the idea and just tweeted your pre-print. Can you expand on the basics of semantic annotation & ontologies more extensively. These are really cool ideas and lines 144-145 just mention the concepts/current usages very briefly only.
thank you very much for your feedback (and the re-tweet!).
We only mention the concepts for semantic annotation, because this is a field of its own and space was limited :). However, we will think about some more detailed explanations for the section you mention. Until then, I would like to recommend you our paper on Controlled vocabularies in Sys Bio, Courtot et al: http://www.ncbi.nlm.nih.gov/pubmed/22027554
In your manuscript, you have a long discussion about the differences between using a graph database (with its own query language) and RDF triple stores (using SPARQL as a query language). Since you are using Neo4J, have you experimented with the Neo4J SPARQL plugin? This should allow you to use SPARQL to query the data you are storing. How does this compare to the BioModels SPARQL endpoint (especially in performance)?