Model annotation and discovery with the Physiome Model Repository.
CellML
Epithelial transport
Model discovery
Physiome Model Repository
Semantic annotation
Journal
BMC bioinformatics
ISSN: 1471-2105
Titre abrégé: BMC Bioinformatics
Pays: England
ID NLM: 100965194
Informations de publication
Date de publication:
06 Sep 2019
06 Sep 2019
Historique:
received:
24
03
2019
accepted:
09
07
2019
entrez:
8
9
2019
pubmed:
8
9
2019
medline:
2
11
2019
Statut:
epublish
Résumé
Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner. We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use. The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.
Sections du résumé
BACKGROUND
BACKGROUND
Mathematics and Phy sics-based simulation models have the potential to help interpret and encapsulate biological phenomena in a computable and reproducible form. Similarly, comprehensive descriptions of such models help to ensure that such models are accessible, discoverable, and reusable. To this end, researchers have developed tools and standards to encode mathematical models of biological systems enabling reproducibility and reuse, tools and guidelines to facilitate semantic description of mathematical models, and repositories in which to archive, share, and discover models. Scientists can leverage these resources to investigate specific questions and hypotheses in a more efficient manner.
RESULTS
RESULTS
We have comprehensively annotated a cohort of models with biological semantics. These annotated models are freely available in the Physiome Model Repository (PMR). To demonstrate the benefits of this approach, we have developed a web-based tool which enables users to discover models relevant to their work, with a particular focus on epithelial transport. Based on a semantic query, this tool will help users discover relevant models, suggesting similar or alternative models that the user may wish to explore or use.
CONCLUSION
CONCLUSIONS
The semantic annotation and the web tool we have developed is a new contribution enabling scientists to discover relevant models in the PMR as candidates for reuse in their own scientific endeavours. This approach demonstrates how semantic web technologies and methodologies can contribute to biomedical and clinical research. The source code and links to the web tool are available at https://github.com/dewancse/model-discovery-tool.
Identifiants
pubmed: 31492098
doi: 10.1186/s12859-019-2987-y
pii: 10.1186/s12859-019-2987-y
pmc: PMC6731580
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
457Subventions
Organisme : Medical Technologies Centre of Research Excellence's Doctoral Scholarship
ID : N/A
Organisme : NIBIB NIH HHS
ID : P41 EB023912
Pays : United States
Organisme : NLM NIH HHS
ID : R01 LM011969
Pays : United States
Organisme : Aotearoa Foundation Fellowship
ID : N/A
Organisme : National Institutes of Health grant
ID : R01LM011969
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