A Bayesian Method for Gene Selection in the Weibull Survival Model with Application to DNA Microarray Data

• 발행기관: 한국자료분석학회
• 저자명: Lee, Kyeong Eun
• 간행물 정보: 『Journal of The Korean Data Analysis Society (JKDAS)』Vol.6 No.4, 917~928쪽, 전체 12쪽
• 주제분류: 자연과학 > 통계학
• 파일형태: PDF
• 발행일자: 2004.08.30

영문 초록

In this paper we consider the well-known Weibull regression models for survival analysis. These models are usually used with few covariates and many observations (subjects). But, for a typical setting of gene expression data from DNA microarray, we need to consider the case where the number of covariates p exceeds the number of samples n . For a given vector of response values which are times to event(death or censored times) and p gene expressions(covariates), we address the issue of how to reduce the dimension by selecting the significant genes. This approach enables us to estimate the survival curve when n≪p . In our approach, rather than fixing the number of selected genes, we will assign a prior distribution to this number. The approach creates additional flexibility by allowing the imposition of constraints, such as bounding the dimension via a prior, which in effect works as a penalty. To implement our methodology, we use a Markov Chain Monte Carlo(MCMC) method. We demonstrate the use of the methodology to diffuse large B-cell lymphoma(DLBCL) complementary DNA(cDNA) data and Breast Carcinomas data.

목차

1. Introduction

2. Weibull Regression Model for Gene Selection

3. Examples

4. Discussion

5. Apendix

References

키워드

Bayesian Model Gene Expression Variable Selection Weibull Regression Survival Model