Linked Life Data

19148294

Source:http://linkedlifedata.com/resource/pubmed/id/19148294

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2009-1-16
pubmed:abstractText
Nested effects models (NEMs) are a class of probabilistic models that were designed to reconstruct a hidden signalling structure from a large set of observable effects caused by active interventions into the signalling pathway. We give a more flexible formulation of NEMs in the language of Bayesian networks. Our framework constitutes a natural generalization of the original NEM model, since it explicitly states the assumptions that are tacitly underlying the original version. Our approach gives rise to new learning methods for NEMs, which have been implemented in the R/Bioconductor package nem. We validate these methods in a simulation study and apply them to a synthetic lethality dataset in yeast.
pubmed:commentsCorrections
pubmed:language
eng
pubmed:journal
pubmed:status
PubMed-not-MEDLINE
pubmed:issn
1687-4145
pubmed:author
pubmed:issnType
Print
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
195272
pubmed:year
2009
pubmed:articleTitle
A Bayesian network view on nested effects models.
pubmed:affiliation
Department of Mathematics, Johannes Gutenberg University, Mainz, Germany.
pubmed:publicationType
Journal Article