Linked Life Data

15272434

Source:http://linkedlifedata.com/resource/pubmed/id/15272434

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
1
pubmed:dateCreated
2004-7-23
pubmed:abstractText
We propose a statistical method for estimating a gene network based on Bayesian networks from microarray gene expression data together with biological knowledge including protein-protein interactions, protein-DNA interactions, binding site information, existing literature and so on. Microarray data do not contain enough information for constructing gene networks accurately in many cases. Our method adds biological knowledge to the estimation method of gene networks under a Bayesian statistical framework, and also controls the trade-off between microarray information and biological knowledge automatically. We conduct Monte Carlo simulations to show the effectiveness of the proposed method. We analyze Saccharomyces cerevisiae gene expression data as an application.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Mar
pubmed:issn
0219-7200
pubmed:author
pubmed:copyrightInfo
Copyright Imperial College Press
pubmed:issnType
Print
pubmed:volume
2
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
77-98
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Combining microarrays and biological knowledge for estimating gene networks via bayesian networks.
pubmed:affiliation
Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo, 108-8639, Japan. imoto@ims.u-tokyo.ac.jp
pubmed:publicationType
Journal Article, Evaluation Studies, Validation Studies