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Bayesian hierarchical models for serial analysis of gene expression

Title
Bayesian hierarchical models for serial analysis of gene expression
Authors
Nam S.Lee S.Shin S.Park T.
Ewha Authors
이상혁
SCOPUS Author ID
이상혁scopus
Issue Date
2006
Journal Title
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISSN
0302-9743JCR Link
Citation
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) vol. 4316 LNBI, pp. 29 - 39
Indexed
SCOPUS scopus
Document Type
Conference Paper
Abstract
In the Serial Analysis of Gene Expression (SAGE) analysis, the statistical procedures have been performed after aggregation of observations from the various libraries for the same class. Most studies have not accounted for the within-class variability. The identification of the differentially expressed genes based on the class separation has not been easy because of heteroscedasticity of libraries. We propose a hierarchical Bayesian model that accounts for the within-class variability. The differential expression is measured by a distribution-free silhouette width which was first introduced into the SAGE differential expression analysis. It is shown that the silhouette width is more appropriate and is easier to compute than the error rate. © Springer-Verlag Berlin Heidelberg 2006.
ISBN
3540689702

9783540689706
Appears in Collections:
자연과학대학 > 생명과학전공 > Journal papers
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