Modelling time course gene expression data with finite mixtures of linear additive models
Modelling time course gene expression data with finite mixtures of linear additive models
复制标题
DOI：
10.1093/bioinformatics/btr653
复制
发表时间：
2012-01-15
期刊：
影响因子：
5.8
通讯作者：
Leisch, Friedrich
中科院分区：
文献类型：
--
作者：
Gruen, Bettina;Scharl, Theresa;Leisch, Friedrich
提出了一类线性加性模型的有限混合模型。使用正则似然方法估计回归模型中的组件特定参数。正则化的优点是：(i)预先指定的样条的最大自由度不如非正则化估计那么重要，（ii）对于每个组件单独以自动方式选择合适的自由度。性能是在模拟研究与人工数据以及酵母细胞周期数据集的基因表达水平随时间的评估。
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.