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
Descripción
Sumario: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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