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

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Etat: Public
Version: de l'auteur
ID Serval
serval:BIB_DDD70CD6595B
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
A novel Mixture Model Method for identification of differentially expressed genes from DNA microarray data.
Périodique
BMC Bioinformatics
Auteur(s)
Najarian K., Zaheri M., Rad A.A., Najarian S., Dargahi J.
ISSN
1471-2105
Statut éditorial
Publié
Date de publication
2004
Peer-reviewed
Oui
Volume
5
Pages
201
Langue
anglais
Notes
Publication types: Journal Article
Résumé
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.
Mots-clé
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
Oui
Création de la notice
29/07/2008 11:16
Dernière modification de la notice
20/08/2019 17:02
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