Gaussian process regression for survival time prediction with genome-wide gene expression.

ABSTRACT: 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 expression on survival are both weak and abundant, so identifying non-zero effects with reasonable accuracy is difficult. As an alternative to methods that use variable selection, we propose a Gaussian process accelerated failure time model to predict survival time using genome-wide or pathway-wide gene expression data. Using a Monte Carlo expectation-maximization algorithm, we jointly impute censored log-survival time and estimate model parameters. We demonstrate the performance of our method and its advantage over existing methods in both simulations and real data analysis. The real data that we analyze were collecte

SUBMITTER: Molstad AJ 

PROVIDER: S-EPMC7846191 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature