InterOnto – Ranking Inter-Ontology Links

Trißl, Silke; Hussels, Philipp; Leser, Ulf

doi:10.1007/978-3-642-31040-9_2

Cited by 2 publications

(2 citation statements)

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“…Method 4 is a bottom-up clustering that addresses the problem of overgeneralization. It uses an adaptation of the scoring function as described in [ 23 ]: …”

Section: Resultsmentioning

confidence: 99%

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Annotation-based feature extraction from sets of SBML models

et al. 2015

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BackgroundModel 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.ResultsIn 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.ConclusionsAnnotation-based feature extraction enables the comparison of model sets, as opposed to existing methods for model-to-keyword comparison, or model-to-model comparison.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0014-4) contains supplementary material, which is available to authorized users.

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“…Method 4 is a bottom-up clustering that addresses the problem of overgeneralization. It uses an adaptation of the scoring function as described in [ 23 ]: …”

Section: Resultsmentioning

confidence: 99%

“…The Information Content can be used to address the problem of overgeneralization when using parent concepts as representatives for child concepts [ 23 ]. The challenge of feature extraction in ontologies is to find summarizing features that do not generalize too strongly.…”

Section: Introductionmentioning

confidence: 99%

Annotation-based feature extraction from sets of SBML models

et al. 2015

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Annotation-Based Feature Extraction from Sets of SBML Models

Alm

Waltemath

Wolkenauer

et al. 2014

Lecture Notes in Computer Science

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Background: 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.

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InterOnto – Ranking Inter-Ontology Links

Cited by 2 publications

References 21 publications

Annotation-based feature extraction from sets of SBML models

Annotation-based feature extraction from sets of SBML models

Annotation-Based Feature Extraction from Sets of SBML Models

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