Linked Life Data

19644169

Source:http://linkedlifedata.com/resource/pubmed/id/19644169

Statements in which the resource exists as a subject.
PredicateObject
rdf:type
lifeskim:mentions
pubmed:issue
3
pubmed:dateCreated
2009-7-31
pubmed:abstractText
In this paper, the extended Kalman filter (EKF) algorithm is applied to model the gene regulatory network from gene time series data. The gene regulatory network is considered as a nonlinear dynamic stochastic model that consists of the gene measurement equation and the gene regulation equation. After specifying the model structure, we apply the EKF algorithm for identifying both the model parameters and the actual value of gene expression levels. It is shown that the EKF algorithm is an online estimation algorithm that can identify a large number of parameters (including parameters of nonlinear functions) through iterative procedure by using a small number of observations. Four real-world gene expression data sets are employed to demonstrate the effectiveness of the EKF algorithm, and the obtained models are evaluated from the viewpoint of bioinformatics.
pubmed:grant
pubmed:language
eng
pubmed:journal
pubmed:citationSubset
IM
pubmed:status
MEDLINE
pubmed:issn
1557-9964
pubmed:author
pubmed:issnType
Electronic
pubmed:volume
6
pubmed:owner
NLM
pubmed:authorsComplete
Y
pubmed:pagination
410-9
pubmed:meshHeading
pubmed:articleTitle
An extended Kalman filtering approach to modeling nonlinear dynamic gene regulatory networks via short gene expression time series.
pubmed:affiliation
Department of Information Systems and Computing, Brunel University, Uxbridge, Middlesex UB83PH, UK. zidong.wang@brunel.ac.uk
pubmed:publicationType
Journal Article, Research Support, Non-U.S. Gov't