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

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

doi:10.1093/biostatistics/kxl026

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

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

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Abstract

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.

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Animals Bayes Theorem Biometry Cluster Analysis Computational Biology Gene Expression Profiling Linear Models Male Mice Models, Genetic Models, Statistical Multigene Family Prostatic Neoplasms Time Factors Journal Article Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

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Lurdes Y T Inoue, Mauricio Neira, Colleen Nelson, Martin Gleave, Ruth Etzioni. 2006-09-15. Cluster-based network model for time-course gene expression data.. https://doi.org/10.1093/biostatistics%2Fkxl026

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