Abstract Qihua Tan^1,2, Mads Thomassen¹ and Torben A. Kruse¹

¹Department of Biochemistry, Pharmacology and Genetics, Odense University Hospital, Odense, Denmark. ²Department of epidemiology, Institute of Public Health, University of Southern Denmark, Odense, Denmark.

Abstract: Among the major issues in gene expression profile classification, feature selection is an important and necessary step in achieving and creating good classification rules given the high dimensionality of microarray data. Although different feature selection methods have been reported, there has been no method specifically proposed for paired microarray experiments. In this paper, we introduce a simple procedure based on a modifi ed t-statistic for feature selection to microarray experiments using the popular matched case-control design and apply to our recent study on tumor metastasis in a low malignant group of breast cancer patients for selecting genes that best predict metastases. Gene or feature selection is optimized by thresholding in a leaving one-pair out cross-validation. Model comparison through empirical application has shown that our method manifests improved efficiency with high sensitivity and specificity.

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