Cluster-based network model for time-course gene expression data |
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Authors: | Inoue Lurdes Y T Neira Mauricio Nelson Colleen Gleave Martin Etzioni Ruth |
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Affiliation: | Department of Biostatistics, University of Washington, F-600 Health Sciences Building, Campus Mail Stop 357232, Seattle, WA 98195, USA. linoue@u.washington.edu |
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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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Keywords: | Bayesian network Bioinformatics Dynamic linear model Mixture model Model-based clustering Prostate cancer Time-course gene expression |
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