Linked Life Data

19505944

Source:http://linkedlifedata.com/resource/pubmed/id/19505944

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
15
pubmed:dateCreated
2009-7-20
pubmed:abstractText
Mathematical description of biological reaction networks by differential equations leads to large models whose parameters are calibrated in order to optimally explain experimental data. Often only parts of the model can be observed directly. Given a model that sufficiently describes the measured data, it is important to infer how well model parameters are determined by the amount and quality of experimental data. This knowledge is essential for further investigation of model predictions. For this reason a major topic in modeling is identifiability analysis.
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:month
Aug
pubmed:issn
1367-4811
pubmed:author
pubmed:issnType
Electronic
pubmed:day
1
pubmed:volume
25
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
1923-9
pubmed:meshHeading
pubmed:year
2009
pubmed:articleTitle
Structural and practical identifiability analysis of partially observed dynamical models by exploiting the profile likelihood.
pubmed:affiliation
Physics Institute, University of Freiburg, 79104 Freiburg, Germany. andreas.raue@me.com
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't