Abstract Motivation: We propose a Bayesian ensemble method for survival prediction in high-
dimensional gene expression data. We specify a fully Bayesian hierarchical approach …

Background Microarray technology is increasingly used to identify potential biomarkers for
cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian …

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 …

Motivation: 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 …

Accurate prognostic prediction using molecular information is a challenging area of
research, which is essential to develop precision medicine. In this paper, we develop …

We propose methods to integrate data across several genomic platforms using a
hierarchical Bayesian analysis framework that incorporates the biological relationships …

Knowledge of transcription of the human genome might greatly enhance our understanding
of cancer. In particular, gene expression may be used to predict the survival of cancer …

Due to the large variability in survival times between cancer patients and the plethora of
genes on microarrays unrelated to outcome, building accurate prediction models that are …

Selection of significant genes via expression patterns is important in a microarray problem.
Owing to small sample size and large number of variables (genes), the selection process …

Background Microarray techniques survey gene expressions on a global scale. Extensive
biomedical studies have been designed to discover subsets of genes that are associated …