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ProbOnto: ontology and knowledge base of probability distributions

Motivation: Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources ex...

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Detalles Bibliográficos
Autores principales: Swat, Maciej J., Grenon, Pierre, Wimalaratne, Sarala
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013898/
https://www.ncbi.nlm.nih.gov/pubmed/27153608
http://dx.doi.org/10.1093/bioinformatics/btw170
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author Swat, Maciej J.
Grenon, Pierre
Wimalaratne, Sarala
author_facet Swat, Maciej J.
Grenon, Pierre
Wimalaratne, Sarala
author_sort Swat, Maciej J.
collection PubMed
description Motivation: Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources exist, no suitably detailed and complex ontology exists nor any database allowing programmatic access. Results: ProbOnto, is an ontology-based knowledge base of probability distributions, featuring more than 80 uni- and multivariate distributions with their defining functions, characteristics, relationships and re-parameterization formulas. It can be used for model annotation and facilitates the encoding of distribution-based models, related functions and quantities. Availability and Implementation: http://probonto.org Contact: mjswat@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-50138982016-09-12 ProbOnto: ontology and knowledge base of probability distributions Swat, Maciej J. Grenon, Pierre Wimalaratne, Sarala Bioinformatics Applications Notes Motivation: Probability distributions play a central role in mathematical and statistical modelling. The encoding, annotation and exchange of such models could be greatly simplified by a resource providing a common reference for the definition of probability distributions. Although some resources exist, no suitably detailed and complex ontology exists nor any database allowing programmatic access. Results: ProbOnto, is an ontology-based knowledge base of probability distributions, featuring more than 80 uni- and multivariate distributions with their defining functions, characteristics, relationships and re-parameterization formulas. It can be used for model annotation and facilitates the encoding of distribution-based models, related functions and quantities. Availability and Implementation: http://probonto.org Contact: mjswat@ebi.ac.uk Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2016-09-01 2016-04-03 /pmc/articles/PMC5013898/ /pubmed/27153608 http://dx.doi.org/10.1093/bioinformatics/btw170 Text en © The Author 2016. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Swat, Maciej J.
Grenon, Pierre
Wimalaratne, Sarala
ProbOnto: ontology and knowledge base of probability distributions
title ProbOnto: ontology and knowledge base of probability distributions
title_full ProbOnto: ontology and knowledge base of probability distributions
title_fullStr ProbOnto: ontology and knowledge base of probability distributions
title_full_unstemmed ProbOnto: ontology and knowledge base of probability distributions
title_short ProbOnto: ontology and knowledge base of probability distributions
title_sort probonto: ontology and knowledge base of probability distributions
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5013898/
https://www.ncbi.nlm.nih.gov/pubmed/27153608
http://dx.doi.org/10.1093/bioinformatics/btw170
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