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

Identifying patient-specific prognostic biomarkers is of critical importance in developing
personalized treatment for clinically and molecularly heterogeneous diseases such as …

We consider the problem where the data consist of a survival time and a binary outcome
measurement for each individual, as well as corresponding predictors. The goal is to select …

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

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 …

Heterogeneity in terms of tumor characteristics, prognosis, and survival among cancer
patients has been a persistent problem for many decades. Currently, prognosis and …

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 …

Proteomic data provide a direct readout of protein function, thus constituting an information-
rich resource for prognostic and predictive modeling. However, protein array data may not …

Predicting the survival time of a cancer patient based on his/her genome-wide gene
expression remains a challenging problem. For certain types of cancer, the effects of gene …

Background Group structures among genes encoded in functional relationships or biological
pathways are valuable and unique features in large-scale molecular data for survival …