![](/images/graphics-bg.png)
A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels : An Application to Escherichia coli Bacterium Data
Joint Authors
Cobre, Juliana
Milan, Luís A.
Saraiva, Erlandson F.
Meira, Silvana
Louzada, Francisco
Source
Computational and Mathematical Methods in Medicine
Issue
Vol. 2012, Issue 2012 (31 Dec. 2012), pp.1-13, 13 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2012-03-05
Country of Publication
Egypt
No. of Pages
13
Main Subjects
Abstract EN
A common interest in gene expression data analysis is to identify from a large pool of candidate genes the genes that present significant changes in expression levels between a treatment and a control biological condition.
Usually, it is done using a statistic value and a cutoff value that are used to separate the genes differentially and nondifferentially expressed.
In this paper, we propose a Bayesian approach to identify genes differentially expressed calculating sequentially credibility intervals from predictive densities which are constructed using the sampled mean treatment effect from all genes in study excluding the treatment effect of genes previously identified with statistical evidence for difference.
We compare our Bayesian approach with the standard ones based on the use of the t-test and modified t-tests via a simulation study, using small sample sizes which are common in gene expression data analysis.
Results obtained report evidence that the proposed approach performs better than standard ones, especially for cases with mean differences and increases in treatment variance in relation to control variance.
We also apply the methodologies to a well-known publicly available data set on Escherichia coli bacterium.
American Psychological Association (APA)
Saraiva, Erlandson F.& Louzada, Francisco& Milan, Luís A.& Meira, Silvana& Cobre, Juliana. 2012. A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels : An Application to Escherichia coli Bacterium Data. Computational and Mathematical Methods in Medicine،Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-511019
Modern Language Association (MLA)
Saraiva, Erlandson F.…[et al.]. A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels : An Application to Escherichia coli Bacterium Data. Computational and Mathematical Methods in Medicine No. 2012 (2012), pp.1-13.
https://search.emarefa.net/detail/BIM-511019
American Medical Association (AMA)
Saraiva, Erlandson F.& Louzada, Francisco& Milan, Luís A.& Meira, Silvana& Cobre, Juliana. A Bayesian Approach for Decision Making on the Identification of Genes with Different Expression Levels : An Application to Escherichia coli Bacterium Data. Computational and Mathematical Methods in Medicine. 2012. Vol. 2012, no. 2012, pp.1-13.
https://search.emarefa.net/detail/BIM-511019
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-511019