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Modelling time course gene expression data with finite mixtures of linear additive models.
- Source :
-
Bioinformatics . Jan2012, Vol. 28 Issue 2, p222-228. 7p. - Publication Year :
- 2012
-
Abstract
- Summary: A model class of finite mixtures of linear additive models is presented. The component-specific parameters in the regression models are estimated using regularized likelihood methods. The advantages of the regularization are that (i) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (ii) for each component individually a suitable degree of freedom is selected in an automatic way. The performance is evaluated in a simulation study with artificial data as well as on a yeast cell cycle dataset of gene expression levels over time.Availability: The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).Contact: Bettina.Gruen@jku.at [ABSTRACT FROM PUBLISHER]
Details
- Language :
- English
- ISSN :
- 13674803
- Volume :
- 28
- Issue :
- 2
- Database :
- Academic Search Index
- Journal :
- Bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 70438618
- Full Text :
- https://doi.org/10.1093/bioinformatics/btr653