首页   按字顺浏览 期刊浏览 卷期浏览 Applications of Bayesian Statistical Methods in Microarray Data Analysis
Applications of Bayesian Statistical Methods in Microarray Data Analysis

 

作者: Dongyan Yang,   Stanislav O Zakharkin,   Grier P Page,   Jacob P L Brand,   Jode W Edwards,   Alfred A Bartolucci,   David B Allison,  

 

期刊: American Journal of PharmacoGenomics  (ADIS Available online 2004)
卷期: Volume 4, issue 1  

页码: 53-62

 

ISSN:1175-2203

 

年代: 2004

 

出版商: ADIS

 

关键词: Bioinformatics

 

数据来源: ADIS

 

摘要:

Microarray technology allows one to measure gene expression levels simultaneously on the whole-genome scale. The rapid progress generates both a great wealth of information and challenges in making inferences from such massive data sets. Bayesian statistical modeling offers an alternative approach to frequentist methodologies, and has several features that make these methods advantageous for the analysis of microarray data. These include the incorporation of prior information, flexible exploration of arbitrarily complex hypotheses, easy inclusion of nuisance parameters, and relatively well developed methods to handle missing data.Recent developments in Bayesian methodology generated a variety of techniques for the identification of differentially expressed genes, finding genes with similar expression profiles, and uncovering underlying gene regulatory networks. Bayesian methods will undoubtedly become more common in the future because of their great utility in microarray analysis.

 

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