Linked Life Data

15130938

Source:http://linkedlifedata.com/resource/pubmed/id/15130938

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
14
pubmed:dateCreated
2004-9-21
pubmed:abstractText
Gene expression data have become an instrumental resource in describing the molecular state associated with various cellular phenotypes and responses to environmental perturbations. The utility of expression profiling has been demonstrated in partitioning clinical states, predicting the class of unknown samples and in assigning putative functional roles to previously uncharacterized genes based on profile similarity. However, gene expression profiling has had only limited success in identifying therapeutic targets. This is partly due to the fact that current methods based on fold-change focus only on single genes in isolation, and thus cannot convey causal information. In this paper, we present a technique for analysis of expression data in a graph-theoretic framework that relies on associations between genes. We describe the global organization of these networks and biological correlates of their structure. We go on to present a novel technique for the molecular characterization of disparate cellular states that adds a new dimension to the fold-based methods and conclude with an example application to a human medulloblastoma dataset.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Sep
pubmed:issn
1367-4803
pubmed:author
pubmed:issnType
Print
pubmed:day
22
pubmed:volume
20
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
2242-50
pubmed:dateRevised
2008-11-21
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Gene co-expression network topology provides a framework for molecular characterization of cellular state.
pubmed:affiliation
Xpogen, Inc., 1340 Centre Street, Newton Centre, MA 02459, USA.
pubmed:publicationType
Journal Article, Comparative Study, Evaluation Studies, Validation Studies