Name
Abstract | ||
|---|---|---|
An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local FDR (false discovery rate) is provided for each gene. An attractive feature of the mixture model approach is that it provides a framework for the estimation of the prior probability that a gene is not differentially expressed, and this probability can subsequently be used in forming a decision rule. The rule can also be formed to take the false negative rate into account. We apply this approach to a well-known publicly available data set on breast cancer, and discuss our findings with reference to other approaches. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1142/S0129065706000755 | INTERNATIONAL JOURNAL OF NEURAL SYSTEMS |
Keywords | Field | DocType |
decision rule,false discovery rate,breast cancer,multiple hypothesis testing,candidate gene,mixture model | Decision rule,Data mining,False discovery rate,Candidate gene,Computer science,Multiple comparisons problem,Prior probability,Gene expression profiling,Mixture model,DNA microarray | Journal |
Volume | Issue | ISSN |
16 | 5 | 0129-0657 |
Citations | PageRank | References |
3 | 0.45 | 3 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Ben-Tovim Jones | 1 | 142 | 9.50 |
| Richard Bean | 2 | 369 | 33.05 |
| McLachlan Geoffrey J. | 3 | 1787 | 126.70 |
| Justin Xi Zhu | 4 | 39 | 4.75 |