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ID: 191107.0, MPI für molekulare Genetik / Department of Computational Molecular Biology
Gaussian mixture density estimation applied to microarray data
Authors:Steinhoff, Christine; Müller, Tobias; Nuber, Ulrike A.; Vingron, Martin
Language:English
Place of Publication:Berlin [et al]
Publisher:Springer
Date of Publication (YYYY-MM-DD):2003
Title of Series:Lecture Notes in Computer Sciences
Volume (in Series or Journal):2810
Physical Description
(e.g. Total Number of Pages):
624 pp
Name of Conference/Meeting:5th International Symposium on Intelligent Data Analysis, IDA 2003
Place of Conference/Meeting:Berlin, Germany
(Start) Date of Conference 
(YYYY-MM-DD):
2003-08-28
End Date of Conference
(YYYY-MM-DD):
2003-08-30
Audience:Experts Only
Abstract / Description:Several publications have focused on fitting a specific distribution to overall microarray data. Due to a number of biological features the distribution of overall spot intensities can take various shapes. It appears to be impossible to find a specific distribution fitting all experiments even if they are carried out perfectly. Therefore, a probabilistic representation that models a mixture of various effects would be suitable. We use a Gaussian mixture model to represent signal intensity profiles. The advantage of this approach is the derivation of a probabilistic criterion for expressed and non-expressed genes. Furthermore our approach does not involve any prior decision on the number of model parameters. We properly fit microarray data of various shapes by a mixture of Gaussians using the EM algorithm and determine the complexity of the mixture model by the Bayesian Information Criterion (BIC). Finally, we apply our method to simulated data and to biological data.
Comment of the Author/Creator:Date: 2003
External Publication Status:published
Document Type:Proceedings
Communicated by:Martin Vingron
Affiliations:MPI für molekulare Genetik
External Affiliations:Max Planck Inst Mol Genet, D-14195 Berlin, Germany.; Univ Wurzburg, Dept Bioinformat, BioCtr, D-97074 Wurzburg, Germany
Identifiers:ISI:000186104900039 [ID No:1]
ISSN:0302-9743 [ID No:2]
ISBN:3-540-40813-4 [ID No:3]
DOI:10.1007/b13240 [ID No:4]
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