Cluster-based network model for time-course gene expression data

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.