Bayesian variable selection for probit mixed models applied to gene selection

Bayesian Variable Selection for Probit Mixed Models Applied to Gene Selection

In computational biology, gene expression datasets are characterized by very few individual samples compared to a large number of measurements per sample. Thus, it is appealing to merge these datasets in order to increase the number of observations and diversify the data, allowing a more reliable selection of genes relevant to the biological problem. Besides, the increased size of a merged data...

Bayesian Variable Selection for Probit Mixed Models

In computational biology, gene expression datasets are characterized by very few individual samples compared to a large number of measurments per sample. Thus, it is appealing to merge these datasets in order to increase the number of observations and diversify the data, allowing a more reliable selection of genes relevant to the biological problem. This necessitates the introduction of the dat...

Gene selection: a Bayesian variable selection approach

UNLABELLED Selection of significant genes via expression patterns is an important problem in microarray experiments. Owing to small sample size and the large number of variables (genes), the selection process can be unstable. This paper proposes a hierarchical Bayesian model for gene (variable) selection. We employ latent variables to specialize the model to a regression setting and uses a Baye...

A Bayesian approach to nonlinear probit gene selection and classification

We consider the problem of gene selection and classification based on the expression data. Specifically, we propose a bootstrap Bayesian gene selection method for nonlinear probit regression. A binomial probit regression model with data augmentation is used to transform the binomial problem into a sequence of smoothing problems. The probit regressor is approximated as a nonlinear combination of...

Bayesian variable selection in linear mixed effects models