Title

Modelling time course gene expression data with finite mixtures of linear additive models

RIS ID

48410

Publication Details

Grun, B., Scharl, T. & Leisch, F. (2011). Modelling time course gene expression data with finite mixtures of linear additive models. Bioinformatics, 28 (2), 222-228.

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 (1) the pre-specified maximum degrees of freedom for the splines is less crucial than for unregularized estimation and that (2) 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 data set of gene expression levels over time.

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Link to publisher version (DOI)

http://dx.doi.org/10.1093/bioinformatics/btr653