Linked Life Data

18601881

Source:http://linkedlifedata.com/resource/pubmed/id/18601881

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
6-7
pubmed:dateCreated
2008-7-8
pubmed:abstractText
The cell is a very complex system and although molecular biology allowed spectacular progresses, biologists are currently trying to tackle complexity at the system level, to unravel cell mysteries. The inference of transcriptional regulatory networks, the characterization of their topology, their dynamic, their role in major cell functions is at the heart of this strategy. We are proposing a systemic approach, based on integration of large scale data such as expression profiling, ChIP on chip analysis and high throughput RNA interference using siRNA microarrays to infer these networks and characterize associated phenotypes. Ultimately, the characterization of the properties of these networks should impact our understanding of biology and offer potential applications in medicine and pharmacology.
pubmed:language
fre
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:issn
0767-0974
pubmed:author
pubmed:issnType
Print
pubmed:volume
24
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
629-34
pubmed:meshHeading
pubmed:articleTitle
[Inference of transcriptional regulatory networks].
pubmed:affiliation
CEA, DSV, IRCM, Laboratoire d'Exploration Fonctionnelle des Génomes, Evry, France. xavier.gidrol@cea.fr
pubmed:publicationType
Journal Article, English Abstract