Annotation-based feature extraction from sets of SBML models

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dc.contributor.authorAlm, Rebekkaen_ZA
dc.contributor.authorWaltemath, Dagmaren_ZA
dc.contributor.authorWolfien, Markusen_ZA
dc.contributor.authorWolkenhauer, Olafen_ZA
dc.contributor.authorHenkel, Ronen_ZA
dc.date.accessioned2016-10-06T06:39:41Z
dc.date.available2016-10-06T06:39:41Z
dc.date.issued2015-04-15
dc.descriptionCITATION: Alm, R. et al. 2015. Annotation-based feature extraction from sets of SBML models. Journal of Biomedical Semantics, 6:20, doi:10.1186/s13326-015-0014-4.
dc.descriptionThe original publication is available from: http://jbiomedsem.biomedcentral.com
dc.description.abstractBackground: Model repositories such as BioModels Database provide computational models of biological systems for the scientific community. These models contain rich semantic annotations that link model entities to concepts in well-established bio-ontologies such as Gene Ontology. Consequently, thematically similar models are likely to share similar annotations. Based on this assumption, we argue that semantic annotations are a suitable tool to characterize sets of models. These characteristics improve model classification, allow to identify additional features for model retrieval tasks, and enable the comparison of sets of models. Results: In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SBML format which were composed from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies, namely Gene Ontology, ChEBI and SBO. We find that three out of the methods are suitable to determine characteristic features for arbitrary sets of models: The selected features vary depending on the underlying model set, and they are also specific to the chosen model set. We show that the identified features map on concepts that are higher up in the hierarchy of the ontologies than the concepts used for model annotations. Our analysis also reveals that the information content of concepts in ontologies and their usage for model annotation do not correlate. Conclusions: Annotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.en_ZA
dc.description.urihttp://jbiomedsem.biomedcentral.com/articles/10.1186/s13326-015-0014-4
dc.description.versionPublisher's version
dc.format.extent13 pages
dc.identifier.citationAlm, R. et al. 2015. Annotation-based feature extraction from sets of SBML models. Journal of Biomedical Semantics, 6:20, doi:10.1186/s13326-015-0014-4.
dc.identifier.issn2041-1480 (online)
dc.identifier.otherdoi:10.1186/s13326-015-0014-4
dc.identifier.urihttp://hdl.handle.net/10019.1/99698
dc.language.isoen_ZAen_ZA
dc.publisherBioMed Central
dc.rights.holderAuthors retain copyrighten_ZA
dc.subjectGenes -- Biological modelsen_ZA
dc.subjectBiological modelsen_ZA
dc.subjectOntologies (Information retrieval)en_ZA
dc.subjectBiological systemsen_ZA
dc.subjectSBML
dc.subjectSemantic annotationsen_ZA
dc.titleAnnotation-based feature extraction from sets of SBML modelsen_ZA
dc.typeArticleen_ZA
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