Linked Life Data

12134151

Source:http://linkedlifedata.com/resource/pubmed/id/12134151

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
4
pubmed:dateCreated
2002-7-31
pubmed:abstractText
Standard clustering methods can classify genes successfully when applied to relatively small data sets, but have limited use in the analysis of large-scale expression data, mainly owing to their assignment of a gene to a single cluster. Here we propose an alternative method for the global analysis of genome-wide expression data. Our approach assigns genes to context-dependent and potentially overlapping 'transcription modules', thus overcoming the main limitations of traditional clustering methods. We use our method to elucidate regulatory properties of cellular pathways and to characterize cis-regulatory elements. By applying our algorithm systematically to all of the available expression data on Saccharomyces cerevisiae, we identify a comprehensive set of overlapping transcriptional modules. Our results provide functional predictions for numerous genes, identify relations between modules and present a global view on the transcriptional network.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1061-4036
pubmed:author
pubmed:issnType
Print
pubmed:volume
31
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
370-7
pubmed:dateRevised
2006-11-15
pubmed:meshHeading
pubmed:year
2002
pubmed:articleTitle
Revealing modular organization in the yeast transcriptional network.
pubmed:affiliation
Department of Molecular Genetics, Weizmann Institute of Science, Rehovot, 76100, Israel.
pubmed:publicationType
Journal Article, Research Support, U.S. Gov't, P.H.S., Research Support, Non-U.S. Gov't