English
 
User Manual Privacy Policy Disclaimer Contact us
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
  Gaussian mixture density estimation applied to microarray data

Steinhoff, C., Müller, T., Nuber, U. A., & Vingron, M. (2003). Gaussian mixture density estimation applied to microarray data. Berlin [et al]: Springer.

Item is

Basic

show hide
Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8B3C-2 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-8B3D-F
Genre: Proceedings

Files

show Files

Locators

show

Creators

show
hide
 Creators:
Steinhoff, Christine1, Author              
Müller, Tobias2, Author
Nuber, Ulrike A.3, Author              
Vingron, Martin4, Author              
Affiliations:
1Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              
2Max Planck Society, ou_persistent13              
3Dept. of Human Molecular Genetics (Head: Hans-Hilger Ropers), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433549              
4Gene regulation (Martin Vingron), Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1479639              

Content

show
hide
Free keywords: -
 Abstract: 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.

Details

show
hide
Language(s): eng - English
 Dates: 2003
 Publication Status: Published in print
 Pages: 624 pp
 Publishing info: Berlin [et al] : Springer
 Table of Contents: -
 Rev. Method: -
 Identifiers: eDoc: 191107
ISI: 000186104900039
ISSN: 0302-9743
ISBN: 3-540-40813-4
DOI: 10.1007/b13240
 Degree: -

Event

show
hide
Title: 5th International Symposium on Intelligent Data Analysis, IDA 2003
Place of Event: Berlin, Germany
Start-/End Date: 2003-08-28 - 2003-08-30

Legal Case

show

Project information

show

Source 1

show
hide
Title: Lecture Notes in Computer Sciences
Source Genre: Series
 Creator(s):
Affiliations:
Publ. Info: -
Pages: - Volume / Issue: 2810 Sequence Number: - Start / End Page: - Identifier: -