Linked Life Data

18664467

Source:http://linkedlifedata.com/resource/pubmed/id/18664467

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
18
pubmed:dateCreated
2008-9-8
pubmed:abstractText
The objective of the present article is to propose and evaluate a probabilistic approach based on Bayesian networks for modelling non-homogeneous and non-linear gene regulatory processes. The method is based on a mixture model, using latent variables to assign individual measurements to different classes. The practical inference follows the Bayesian paradigm and samples the network structure, the number of classes and the assignment of latent variables from the posterior distribution with Markov Chain Monte Carlo (MCMC), using the recently proposed allocation sampler as an alternative to RJMCMC.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
15
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2071-8
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2008
pubmed:articleTitle
Modelling non-stationary gene regulatory processes with a non-homogeneous Bayesian network and the allocation sampler.
pubmed:affiliation
School of Biological Sciences, The University of Edinburgh, Swann Building, King's Buildings, Edinburgh, UK. marco@bioss.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't