biostudies-literatureUnknown6Alm R<h4>Background</h4>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.<h4>Results</h4>In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in SJournal of biomedical semantics20https://www.ebi.ac.uk/biostudies/studies/S-EPMC4405863biostudies-literatureAnnotation-based feature extraction from sets of SBML models.PMC4405863Henkel RAlm RWolkenhauer OWolfien MWaltemath DfalseAnnotation-based feature extraction from sets of SBML models.<h4>Background</h4>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.<h4>Results</h4>In this paper we discuss four methods for annotation-based feature extraction from model sets. We tested all methods on sets of models in S2015-01-01T00:00:00Z20152025-04-18T15:04:21.46Z2019-03-27T01:50:19ZS-EPMC44058632590499710.1186/s13326-015-0014-4