Online Program Home
My Program

Abstract Details

Activity Number: 479 - Survival Analysis II
Type: Contributed
Date/Time: Wednesday, August 1, 2018 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract #330120 Presentation
Title: Genome-Wide Gaussian Process Regression for Survival Time Prediction
Author(s): Aaron J. Molstad* and Wei Sun and Li Hsu
Companies: Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center and Fred Hutchinson Cancer Research Center, USA
Keywords: Genomics; Gaussian process; Monte Carlo EM algorithm; Survival anaylsis

Predicting the survival time of a cancer patient based on their genome-wide gene expression remains a challenging problem. In particular, for certain types of cancer, the effects of gene-expression are both weak and abundant, so identifying nonzero effects with reasonable accuracy is difficult. As an alternative to the existing methods, we propose a Gaussian process accelerated failure time model. Using a Monte-Carlo EM algorithm, we impute censored failure times and estimate model parameters jointly via maximum likelihood. We demonstrate our method's accuracy in predicting the survival times of patients with kidney renal clear cell carcinoma based on the expression of more than 20,000 genes. The proposed method is broadly applicable as it can accommodate right, left, and interval censoring; and provides a simple way to integrate multiple types of omics data.

Authors who are presenting talks have a * after their name.

Back to the full JSM 2018 program