The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data
Joint Authors
Source
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-10-07
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Natural & Life Sciences (Multidisciplinary)
Biology
Abstract EN
We present a statistical method to rank observed genes in gene expression time series experiments according to their degree of regulation in a biological process.
The ranking may be used to focus on specific genes or to select meaningful subsets of genes from which gene regulatory networks can be built.
Our approach is based on a state space model that incorporates hidden regulators of gene expression.
Kalman (K) smoothing and maximum (M) likelihood estimation techniques are used to derive optimal estimates of the model parameters upon which a proposed regulation criterion is based.
The statistical power of the proposed algorithm is investigated, and a real data set is analyzed for the purpose of identifying regulated genes in time dependent gene expression data.
This statistical approach supports the concept that meaningful biological conclusions can be drawn from gene expression time series experiments by focusing on strong regulation rather than large expression values.
American Psychological Association (APA)
Bremer, Martina& Doerge, R. W.. 2009. The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data. Advances in Bioinformatics،Vol. 2009, no. 2009, pp.1-10.
https://search.emarefa.net/detail/BIM-460264
Modern Language Association (MLA)
Bremer, Martina& Doerge, R. W.. The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data. Advances in Bioinformatics No. 2009 (2009), pp.1-10.
https://search.emarefa.net/detail/BIM-460264
American Medical Association (AMA)
Bremer, Martina& Doerge, R. W.. The KM-Algorithm Identifies Regulated Genes in Time Series Expression Data. Advances in Bioinformatics. 2009. Vol. 2009, no. 2009, pp.1-10.
https://search.emarefa.net/detail/BIM-460264
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-460264