Cluster-based network model for time-course gene expression data
Cluster-based network model for time-course gene expression data
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DOI：
10.1093/biostatistics/kxl026
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发表时间：
2007-07-01
期刊：
影响因子：
2.1
通讯作者：
Etzioni, Ruth
中科院分区：
文献类型：
--
作者：
Inoue, Lurdes Y. T.;Neira, Mauricio;Etzioni, Ruth
我们提出了一个基于模型的方法来统一聚类和网络建模使用时程基因表达数据。具体来说，我们的方法使用混合模型来聚类基因。同一簇内的基因共享相似的表达谱。该网络是建立在集群特定的表达谱使用状态空间模型。我们讨论了我们的模型的应用程序，以模拟数据，以及从前列腺癌进展的动物模型中产生的时程基因表达数据。后者的应用程序表明，结合统计/生物信息学分析，我们能够提取基因与基因的关系，支持的文献，以及新的合理的关系。
We propose a model-based approach to unify clustering and network modeling using time-course gene expression data. Specifically, our approach uses a mixture model to cluster genes. Genes within the same cluster share a similar expression profile. The network is built over cluster-specific expression profiles using state-space models. We discuss the application of our model to simulated data as well as to time-course gene expression data arising from animal models on prostate cancer progression. The latter application shows that with a combined statistical/bioinformatics analyses, we are able to extract gene-to-gene relationships supported by the literature as well as new plausible relationships.