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SBMate: A Framework for Evaluating Quality of Annotations in Systems Biology Models
Open accessAbstract
The interests in repurposing and reusing systems biology models have been growing in recent years. Semantic annotations play an important role for this, as they provide crucial information on the meanings and functions of models. However, there are a limited number of tools that evaluate the existence or quality of such annotations. In this paper, we introduce SBMate, a python package that would serve as a framework for evaluating the quality of annotations in systems biology models. Three default metrics are provided: coverage, consistency, and specificity. Coverage checks whether annotations exist in a model. Consistency tests if the annotations are appropriate for the given model element. Finally, specificity represents how detailed the annotations are. We analyzed 1,000 curated models from the BioModels repository using the three metrics and discussed the results. Additional metrics can be easily added to extend the current version of SBMate.
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SBMate is a python package that automatically assesses coverage, consistency, and specificity of semantic annotations in systems biology models.
“Some tools already exist for this purpose, such as SBMate [35], a python package that automatically assesses coverage, consistency, and specificity of semantic annotations in systems biology models.”
From Adapting modeling and simulation credibility standards to computational systems biology (2023) · cites this paper as [35]
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References 18 · Cited by 2
Metadata
- Authors
- Shin, W., Hellerstein, J. L., Munarko, Y., Neal, M. L., Nickerson, D. P., Rampadarath, A. K., Sauro, H. M., & Gennari, J. H.
- Affiliations
- University of Auckland (NZ)
- University of Washington (US)
- Seattle Children's Research Institute (US)
- Corresponding author
- Shin, Woosub
- ORCID
- Venue
- bioRxiv (Cold Spring Harbor Laboratory)
- Publisher
- openRxiv
- Published
- 2021-10-09
- OpenAlex
- W3205521089
- Cited by
- 2
- References
- 18
- Citations by year
- 2023: 2
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
- Funders
- National Science Foundation (P41GM109824, 1933453)
- University of Washington (1933453, P41GM109824)
- National Institutes of Health (1933453)
- National Institute of General Medical Sciences (P41GM109824)
- Topics
- Health Sciences › Medicine › Health Informatics › Biomedical Text Mining and Ontologies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene Regulatory Network Analysis
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- semantic annotation, systems biology models, annotation quality evaluation, annotation consistency, biomodels, model reuse, Python library
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