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 …

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 …

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 …

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

Prediction models that use gene expression levels are now being proposed for personalized
treatment of cancer, but building accurate models that are easy to interpret remains a …

Background Cancer survival studies are commonly analyzed using survival-time prediction
models for cancer prognosis. A number of different performance metrics are used to …

High-throughput technologies generate many types of high-dimensional genomic and
proteomics data. Important examples include DNA microarray technology, which permits …

An important goal of DNA microarray research is to develop tools to diagnose cancer more
accurately based on the genetic profile of a tumor. There are several existing techniques in …

We apply Gaussian process (GP) regression, which provides a powerful non-parametric
probabilistic method of relating inputs to outputs, to survival data consisting of time-to-event …

Survival analysis mainly deals with the time to event, including death, onset of disease, and
bankruptcy. The common characteristic of survival analysis is that it contains “censored” …