Data Interpretation, Statistical
Permanent link to this recordhttp://hdl.handle.net/20.500.12613/5613
MetadataShow full item record
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.
Citation to related workPublic Library of Science (PLoS)
Has partPLoS Computational Biology
ADA complianceFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact firstname.lastname@example.org