Title
Iterative Bayesian Model Averaging: A Method for the Application of Survival Analysis to High-Dimensional Microarray Data
Publication Date
2-26-2009
Document Type
Article
Abstract
Microarray technology is increasingly used to identify potential biomarkers for cancer prognostics and diagnostics. Previously, we have developed the iterative Bayesian Model Averaging (BMA) algorithm for use in classification. Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data. The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis) using a small number of selected genes. Our multivariate procedure combines the effectiveness of multiple contending models by calculating the weighted average of their posterior probability distributions. Our results demonstrate that our iterative BMA algorithm for survival analysis achieves high prediction accuracy while consistently selecting a small and cost-effective number of predictor genes.
Publication Title
BMC Bioinformatics
Volume
10
Issue
1
DOI
10.1186/1471-2105-10-72
Publisher Policy
open access
Open Access Status
OA Journal
Recommended Citation
Annest, Amalia; Bumgarner, Roger E.; Raftery, Adrian E.; and Yeung, Ka Yee, "Iterative Bayesian Model Averaging: A Method for the Application of Survival Analysis to High-Dimensional Microarray Data" (2009). School of Engineering and Technology Publications. 299.
https://digitalcommons.tacoma.uw.edu/tech_pub/299
