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dc.creatorAiroldi, EM
dc.date.accessioned2021-02-01T22:05:26Z
dc.date.available2021-02-01T22:05:26Z
dc.date.issued2007-12-01
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/5595
dc.identifier.other18069887 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/5613
dc.description.abstractProbabilistic graphical models (PGMs) have become a popular tool for computational analysis of biological data in a variety of domains. But, what exactly are they and how do they work? How can we use PGMs to discover patterns that are biologically relevant? And to what extent can PGMs help us formulate new hypotheses that are testable at the bench? This Message sketches out some answers and illustrates the main ideas behind the statistical approach to biological pattern discovery. © 2007 Edoardo M. Airoldi.
dc.format.extent2421-2425
dc.language.isoen
dc.relation.haspartPLoS Computational Biology
dc.relation.isreferencedbyPublic Library of Science (PLoS)
dc.rightsCC BY
dc.subjectAlgorithms
dc.subjectComputer Graphics
dc.subjectComputer Simulation
dc.subjectData Interpretation, Statistical
dc.subjectModels, Biological
dc.subjectModels, Statistical
dc.titleGetting started in probabilistic graphical models
dc.typeArticle
dc.type.genreReview
dc.type.genreJournal
dc.relation.doi10.1371/journal.pcbi.0030252
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidAiroldi, Edoardo|0000-0002-3512-0542
dc.date.updated2021-02-01T22:05:23Z
refterms.dateFOA2021-02-01T22:05:26Z


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