title: Modelling time course gene expression data with finite mixtures of linear additive models.
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.
  
  
  BACKGROUND
  The latest release version of the R package flexmix is available from CRAN (http://cran.r-project.org/).
authors: Department of Applied Statistics, Johannes Kepler University Linz, Altenbergerstrasse 69, 4040 Linz, Austria. Bettina.Gruen@jku.at
authors: Bettina
authors: B
authors: Grün
authors: Theresa
authors: T
authors: Scharl
authors: Friedrich
authors: F
authors: Leisch
mesh_terms: D000465:Algorithms; D002453:Cell Cycle; D020869:Gene Expression Profiling; D006801:Humans; D016013:Likelihood Functions; D016014:Linear Models; D008957:Models, Genetic; D012044:Regression Analysis; D012441:Saccharomyces cerevisiae / Q000166:cytology / Q000235:genetics*; D013997:Time Factors