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

Lurdes Y.T. Inoue, Mauricio Neira, Colleen Nelson, Martin Gleave, Ruth Etzioni

Research output: Contribution to journalArticlepeer-review

24 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)507-525
Number of pages19
Issue number3
StatePublished - Jul 2007
Externally publishedYes


  • Bayesian network
  • Bioinformatics
  • Dynamic linear model
  • Mixture model
  • Model-based clustering
  • Prostate cancer
  • Time-course gene expression

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty


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