Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이상혁 | * |
dc.date.accessioned | 2018-06-02T08:13:59Z | - |
dc.date.available | 2018-06-02T08:13:59Z | - |
dc.date.issued | 2006 | * |
dc.identifier.isbn | 3540689702 | * |
dc.identifier.isbn | 9783540689706 | * |
dc.identifier.issn | 0302-9743 | * |
dc.identifier.other | OAK-17820 | * |
dc.identifier.uri | https://dspace.ewha.ac.kr/handle/2015.oak/243954 | - |
dc.description.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. | * |
dc.language | English | * |
dc.title | Bayesian hierarchical models for serial analysis of gene expression | * |
dc.type | Conference Paper | * |
dc.relation.volume | 4316 LNBI | * |
dc.relation.index | SCOPUS | * |
dc.relation.startpage | 29 | * |
dc.relation.lastpage | 39 | * |
dc.relation.journaltitle | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | * |
dc.identifier.scopusid | 2-s2.0-34547431673 | * |
dc.author.google | Nam S. | * |
dc.author.google | Lee S. | * |
dc.author.google | Shin S. | * |
dc.author.google | Park T. | * |
dc.contributor.scopusid | 이상혁(57212112170) | * |
dc.date.modifydate | 20240415122632 | * |