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- W171157798 abstract "We review the use of semiparametric mixture models for Bayesian inference in high-throughput genomic data. We discuss three specific approaches for microarray data, for protein mass spectrometry experiments, and for serial analysis of gene expression (SAGE) data. For the microarray data and the protein mass spectrometry we assume group comparison experiments, that is, experiments that seek to identify genes and proteins that are differentially expressed across two biologic conditions of interest. For the SAGE data example we consider inference for a single biologic sample. For all three applications we use flexible mixture models to implement inference. For the microarray data we define a Dirichlet process mixture of normal model. For the mass spectrometry data we introduce a mixture of Beta model. The proposed inference for SAGE data is based on a semiparametric mixture of Poisson distributions." @default.
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- W171157798 date "2006-07-24" @default.
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- W171157798 title "Bayesian Mixture Models for Gene Expression and Protein Profiles" @default.
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- W171157798 doi "https://doi.org/10.1017/cbo9780511584589.013" @default.
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