Linked Life Data

15262806

Source:http://linkedlifedata.com/resource/pubmed/id/15262806

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:dateCreated
2004-7-20
pubmed:abstractText
MOTIVATION: Genetic networks regulate key processes in living cells. Various methods have been suggested to reconstruct network architecture from gene expression data. However, most approaches are based on qualitative models that provide only rough approximations of the underlying events, and lack the quantitative aspects that are critical for understanding the proper function of biomolecular systems. RESULTS: We present fine-grained dynamical models of gene transcription and develop methods for reconstructing them from gene expression data within the framework of a generative probabilistic model. Unlike previous works, we employ quantitative transcription rates, and simultaneously estimate both the kinetic parameters that govern these rates, and the activity levels of unobserved regulators that control them. We apply our approach to expression datasets from yeast and show that we can learn the unknown regulator activity profiles, as well as the binding affinity parameters. We also introduce a novel structure learning algorithm, and demonstrate its power to accurately reconstruct the regulatory network from those datasets.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
4
pubmed:volume
20 Suppl 1
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
i248-56
pubmed:dateRevised
2009-11-4
pubmed:meshHeading
pubmed:year
2004
pubmed:articleTitle
Inferring quantitative models of regulatory networks from expression data.
pubmed:affiliation
School of Computer Science & Engineering, Hebrew University, Jerusalem, Israel.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural