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Annotation-based feature extraction from sets of SBML models

: Alm, Rebekka; Waltemath, Dagmar; Wolkenauer, Olaf; Henkel, Ron


Galhardas, H.:
Data integration in the life sciences. 10th international conference, DILS 2014 : Lisbon, Portugal, July 17-18, 2014; Proceedings
Cham: Springer International Publishing, 2014 (Lecture Notes in Computer Science 8574)
ISBN: 978-3-319-08589-0 (Print)
ISBN: 978-3-319-08590-6 (Online)
International Conference on Data Integration in the Life Sciences (DILS) <10, 2014, Lisbon>
Conference Paper
Fraunhofer IGD, Institutsteil Rostock ()
Fraunhofer IGD ()
annotation; information retrieval; feature extraction; Business Field: Digital society; Research Area: Human computer interaction (HCI)

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 can then help to classify models, to identify additional features for model retrieval tasks, or to enable the comparison of sets of models. In this paper, we present four methods for annotation-based feature extraction from model sets. All methods have been used with four different model sets in SBML format and taken from BioModels Database. To characterize each of these sets, we analyzed and extracted concepts from three frequently used ontologies for SBML models, namely Gene Ontology, ChEBI and SBO. We find that three of the four tested methods are suitable to determine characteristic features for model sets. 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.