Source:http://linkedlifedata.com/resource/pubmed/id/17825607
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| Predicate | Object |
|---|---|
| rdf:type | |
| lifeskim:mentions | |
| pubmed:dateCreated |
2007-10-30
|
| pubmed:abstractText |
The purpose of the paper is to propose a methodology for learning gene regulatory networks from DNA microarray data based on the integration of different data and knowledge sources. We applied our method to Saccharomyces cerevisiae experiments, focusing our attention on cell cycle regulatory mechanisms. We exploited data from deletion mutant experiments (static data), gene expression time series (dynamic data) and the knowledge encoded in the Gene Ontology.
|
| pubmed:language |
eng
|
| pubmed:journal | |
| pubmed:citationSubset |
IM
|
| pubmed:status |
MEDLINE
|
| pubmed:month |
Dec
|
| pubmed:issn |
1872-8243
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| pubmed:author | |
| pubmed:issnType |
Electronic
|
| pubmed:volume |
76 Suppl 3
|
| pubmed:owner |
NLM
|
| pubmed:authorsComplete |
Y
|
| pubmed:pagination |
S462-75
|
| pubmed:dateRevised |
2009-5-21
|
| pubmed:meshHeading |
pubmed-meshheading:17825607-Algorithms,
pubmed-meshheading:17825607-Artificial Intelligence,
pubmed-meshheading:17825607-Gene Regulatory Networks,
pubmed-meshheading:17825607-Humans,
pubmed-meshheading:17825607-Oligonucleotide Array Sequence Analysis,
pubmed-meshheading:17825607-Saccharomyces cerevisiae
|
| pubmed:year |
2007
|
| pubmed:articleTitle |
Inferring gene regulatory networks by integrating static and dynamic data.
|
| pubmed:affiliation |
Dipartimento di Informatica e Sistemistica, Università degli Studi di Pavia, via Ferrata 1, 27100 Pavia, Italy.
|
| pubmed:publicationType |
Journal Article,
Research Support, Non-U.S. Gov't
|