A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data.

Details

Ressource 1Download: BIB_DDD70CD6595B.P001.pdf (588.75 [Ko])
State: Public
Version: author
Serval ID
serval:BIB_DDD70CD6595B
Type
Article: article from journal or magazin.
Collection
Publications
Title
A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data.
Journal
BMC Bioinformatics
Author(s)
Najarian K., Zaheri M., Rad A.A., Najarian S., Dargahi J.
ISSN
1471-2105
Publication state
Published
Issued date
2004
Peer-reviewed
Oui
Volume
5
Pages
201
Language
english
Notes
Publication types: Journal Article
Abstract
BACKGROUND: The main goal in analyzing microarray data is to determine the genes that are differentially expressed across two types of tissue samples or samples obtained under two experimental conditions. Mixture model method (MMM hereafter) is a nonparametric statistical method often used for microarray processing applications, but is known to over-fit the data if the number of replicates is small. In addition, the results of the MMM may not be repeatable when dealing with a small number of replicates. In this paper, we propose a new version of MMM to ensure the repeatability of the results in different runs, and reduce the sensitivity of the results on the parameters. RESULTS: The proposed technique is applied to the two different data sets: Leukaemia data set and a data set that examines the effects of low phosphate diet on regular and Hyp mice. In each study, the proposed algorithm successfully selects genes closely related to the disease state that are verified by biological information. CONCLUSION: The results indicate 100% repeatability in all runs, and exhibit very little sensitivity on the choice of parameters. In addition, the evaluation of the applied method on the Leukaemia data set shows 12% improvement compared to the MMM in detecting the biologically-identified 50 expressed genes by Thomas et al. The results witness to the successful performance of the proposed algorithm in quantitative pathogenesis of diseases and comparative evaluation of treatment methods.
Keywords
Algorithms, Animals, Cell Line, Tumor, Computational Biology, Computer Simulation, Data Interpretation, Statistical, Gene Expression Profiling, Gene Expression Regulation, Gene Expression Regulation, Neoplastic, Humans, Hypophosphatemia, Mice, Mice, Transgenic, Models, Biological, Models, Statistical, Models, Theoretical, Multivariate Analysis, Numerical Analysis, Computer-Assisted, Oligonucleotide Array Sequence Analysis, Sensitivity and Specificity
Pubmed
Web of science
Open Access
Yes
Create date
29/07/2008 10:16
Last modification date
20/08/2019 16:02
Usage data