Genre
ReviewJournal
Date
2007-12-01Author
Airoldi, EMSubject
AlgorithmsComputer Graphics
Computer Simulation
Data Interpretation, Statistical
Models, Biological
Models, Statistical
Permanent link to this record
http://hdl.handle.net/20.500.12613/5613
Metadata
Show full item recordDOI
10.1371/journal.pcbi.0030252Abstract
Probabilistic 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.Citation to related work
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http://dx.doi.org/10.34944/dspace/5595