SGD Paper 
Grun B, et al. (2012) Modelling time course gene expression data with finite mixtures of linear additive models. Bioinformatics 28(2):222-8
Abstract: 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/).
| Status: Published | Type: Journal Article | PubMed ID: 22121159 |
Topics addressed in this paper
- To find other papers on a gene and topic, click on the colored ball in the appropriate box.
- displays other papers with information about that topic for that gene.
- displays other papers in SGD that are associated with that topic.
The topic is addressed in these papers but does not describe a specific gene or chromosomal feature.
- To go to the Locus page for a gene, click on the gene name.
| Topics | Topics not linked to Genes |
|---|---|
| Computational analysis | 
|
| Omics |
|
