VIBE 2.0: Visual Integration for Bayesian Evaluation

Summary: Data fusion methods are powerful tools for evaluating experiments designed to discover measurable features of directly unobservable systems. We describe an interactive software platform, Visual Integration for Bayesian Evaluation, that ingests or creates Bayesian posterior probability matri...

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Detalles Bibliográficos
Autores principales: Beagley, Nathaniel, Stratton, Kelly G., Webb-Robertson, Bobbie-Jo M.
Formato: Texto
Lenguaje:English
Publicado: Oxford University Press 2010
Materias:
Applications Note
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804300/
https://www.ncbi.nlm.nih.gov/pubmed/19933164
http://dx.doi.org/10.1093/bioinformatics/btp639
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author Beagley, Nathaniel
Stratton, Kelly G.
Webb-Robertson, Bobbie-Jo M.
author_facet Beagley, Nathaniel
Stratton, Kelly G.
Webb-Robertson, Bobbie-Jo M.
author_sort Beagley, Nathaniel
collection PubMed
description Summary: Data fusion methods are powerful tools for evaluating experiments designed to discover measurable features of directly unobservable systems. We describe an interactive software platform, Visual Integration for Bayesian Evaluation, that ingests or creates Bayesian posterior probability matrices, performs data fusion and allows the user to interactively evaluate the classification power of fusing various combinations of data sources, such as transcriptomic, proteomics, metabolomics, biochemistry and function. Availability: http://omics.pnl.gov/software/VIBE.php Contact: bj@pnl.gov Supplementary information: Supplementary data are available at Bioinformatics online.
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spelling pubmed-28043002010-01-12 VIBE 2.0: Visual Integration for Bayesian Evaluation Beagley, Nathaniel Stratton, Kelly G. Webb-Robertson, Bobbie-Jo M. Bioinformatics Applications Note Summary: Data fusion methods are powerful tools for evaluating experiments designed to discover measurable features of directly unobservable systems. We describe an interactive software platform, Visual Integration for Bayesian Evaluation, that ingests or creates Bayesian posterior probability matrices, performs data fusion and allows the user to interactively evaluate the classification power of fusing various combinations of data sources, such as transcriptomic, proteomics, metabolomics, biochemistry and function. Availability: http://omics.pnl.gov/software/VIBE.php Contact: bj@pnl.gov Supplementary information: Supplementary data are available at Bioinformatics online. Oxford University Press 2010-01-15 2009-11-17 /pmc/articles/PMC2804300/ /pubmed/19933164 http://dx.doi.org/10.1093/bioinformatics/btp639 Text en © The Author(s) 2009. Published by Oxford University Press. http://creativecommons.org/licenses/by-nc/2.5/uk/ This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/2.5) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Note
Beagley, Nathaniel
Stratton, Kelly G.
Webb-Robertson, Bobbie-Jo M.
VIBE 2.0: Visual Integration for Bayesian Evaluation
title VIBE 2.0: Visual Integration for Bayesian Evaluation
title_full VIBE 2.0: Visual Integration for Bayesian Evaluation
title_fullStr VIBE 2.0: Visual Integration for Bayesian Evaluation
title_full_unstemmed VIBE 2.0: Visual Integration for Bayesian Evaluation
title_short VIBE 2.0: Visual Integration for Bayesian Evaluation
title_sort vibe 2.0: visual integration for bayesian evaluation
topic Applications Note
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2804300/
https://www.ncbi.nlm.nih.gov/pubmed/19933164
http://dx.doi.org/10.1093/bioinformatics/btp639
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AT strattonkellyg vibe20visualintegrationforbayesianevaluation
AT webbrobertsonbobbiejom vibe20visualintegrationforbayesianevaluation

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