Background: Classification studies using gene expression datasets are usually based on small numbers of samples and tens of thousands of genes. The selection of those genes that are important for distinguishing the different sample classes being compared, poses a challenging problem in high dimensional data analysis. We describe a new procedure for selecting significant genes as recursive cluster elimination (RCE) rather than recursive feature elimination (RFE). We have tested this algorithm on six datasets and compared its performance with that of two related classification procedures with RFE.
In REF , a method for selecting relevant genes in comparative gene expression studies was proposed, referred to as recursive cluster elimination (RCE).
Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data
BMC Bioinformatics
BMC Bioinformatics
Computer Science Medicine