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- W1567334358 abstract "Most of the existing methods for the identification of biologically interesting genes in a temporal expression profiling dataset do not fully exploit the temporal ordering in the dataset and are based on normality assumptions for the gene expression.In this paper, we introduce a Bayesian median regression model to detect genes whose temporal profile is significantly different across a number of biological conditions. The regression model is defined by a polynomial function where both time and condition effects as well as interactions between the two are included. MCMC‐based inference returns the posterior distribution of the polynomial coefficients. From this a simple Bayes factor test is proposed to test for significance.The estimation of the median rather than the mean, and within a Bayesian framework, increases the robustness of the method compared to a Hotelling T2‐test previously suggested. This is shown on simulated data and on muscular dystrophy gene expression data." @default.
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- W1567334358 date "2007-01-01" @default.
- W1567334358 modified "2023-10-17" @default.
- W1567334358 title "Bayesian median regression for temporal gene expression data" @default.
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- W1567334358 doi "https://doi.org/10.1063/1.2793405" @default.
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