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Bayesian variable selection for the analysis of microarray data with censored outcomes.

Bioinformatics, no. 18 (2006): 2262-2268

Cited by: 83|Views1
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Abstract

A common task in microarray data analysis consists of identifying genes associated with a phenotype. When the outcomes of interest are censored time-to-event data, standard approaches assess the effect of genes by fitting univariate survival models. In this paper, we propose a Bayesian variable selection approach, which allows the identif...More

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