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BMCBI
2007

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

9 years 5 days ago
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...
Malik Yousef, Segun Jung, Louise C. Showe, Michael
Added 12 Dec 2010
Updated 12 Dec 2010
Type Journal
Year 2007
Where BMCBI
Authors Malik Yousef, Segun Jung, Louise C. Showe, Michael K. Showe
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