Linked Life Data

16605367

Source:http://linkedlifedata.com/resource/pubmed/id/16605367

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
2 Pt 1
pubmed:dateCreated
2006-4-11
pubmed:abstractText
We introduce a grammar-based hybrid approach to reverse engineering nonlinear ordinary differential equation models from observed time series. This hybrid approach combines a genetic algorithm to search the space of model architectures with a Kalman filter to estimate the model parameters. Domain-specific knowledge is used in a context-free grammar to restrict the search space for the functional form of the target model. We find that the hybrid approach outperforms a pure evolutionary algorithm method, and we observe features in the evolution of the dynamical models that correspond with the emergence of favorable model components. We apply the hybrid method to both artificially generated time series and experimentally observed protein levels from subjects who received the smallpox vaccine. From the observed data, we infer a cytokine protein interaction network for an individual's response to the smallpox vaccine.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:chemical
pubmed:status
MEDLINE
pubmed:month
Feb
pubmed:issn
1539-3755
pubmed:author
pubmed:issnType
Print
pubmed:volume
73
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
021912
pubmed:dateRevised
2007-11-14
pubmed:meshHeading
pubmed:year
2006
pubmed:articleTitle
Hybrid grammar-based approach to nonlinear dynamical system identification from biological time series.
pubmed:affiliation
Department of Pediatrics, Vanderbilt University Medical Center, Nashville, Tennessee 37232, USA.
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't, Research Support, N.I.H., Extramural