CASPAR: a hierarchical bayesian approach to predict survival times in cancer from gene expression data. Motivation: DNA microarrays allow the simultaneous measurement of thousands of gene expression levels in any given patient sample. Gene expression data have been shown to correlate with survival in several cancers, however, analysis of the data is difficult, since typically at most a few hundred patients are available, resulting in severely underdetermined regression or classification models. Several approaches exist to classify patients in different risk classes, however, relatively little has been done with respect to the prediction of actual survival times. We introduce CASPAR, a novel method to predict true survival times for the individual patient based on microarray measurements. CASPAR is based on a multivariate Cox regression model that is embedded in a Bayesian framework. A hierarchical prior distribution on the regression parameters is specifically designed to deal with high dimensionality (large number of genes) and low sample size settings, that are typical for microarray measurements. This enables CASPAR to automatically select small, most informative subsets of genes for prediction.
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Mazur, Johanna: Bayesian inference of gene regulatory networks: From parameter estimation to experimental design (2012)
- Radde, Nicole; Kaderali, Lars: Inference of an oscillating model for the yeast cell cycle (2009)
- van Wieringen, Wessel N.; Kun, David; Hampel, Regina; Boulesteix, Anne-Laure: Survival prediction using gene expression data: a review and comparison (2009)
- Radde, Nicole: Modeling non-linear dynamic phenomena in biochemical networks. (2007)
- Kaderali, Lars: A hierarchical Bayesian approach to regression and its application to predicting survival times in cancer (2006)