Recursive Cluster Elimination (RCE) for classification and feature selection from gene expression data

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. Results: We have developed a novel method for selecting significant genes in comparative gene expression studies. This method, which we refer to as SVM-RCE, combines K-means, a clustering method, to identify correlated gene clusters, and Support Vector Machines (SVMs), a supervised machine learning classification method, to identify and score (rank) those gene clusters for the purpose of class...

• Type: Journal
• Year: 2007
• Where: BMCBI
• Authors: Malik Yousef, Segun Jung, Louise C. Showe, Michael K. Showe
• Keywords: BMCBI 2007, Correlated Genes, Gene Clusters, Significant Genes