Abstract
In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.
Highlights
With the development of DNA microarray technology, biological researchers can pay more attention to simultaneously studying the expression levels of thousands of genes [1,2]
Cancer classification based on gene expression levels is one of the most active topics in genome research, which is appropriate for gene expression levels in different situations [3,4]
The number of genes ranges in the thousands from a hundred or fewer tissue samples, and so gene selection has recently emerged as important technology for cancer classification [6]
Summary
With the development of DNA microarray technology, biological researchers can pay more attention to simultaneously studying the expression levels of thousands of genes [1,2]. Cancer classification using DNA microarray data is a challenge because of the data’s high dimension and small sample size [5]. The number of genes ranges in the thousands from a hundred or fewer tissue samples, and so gene selection has recently emerged as important technology for cancer classification [6]. Effective gene selection methods can be desirable to help to classify different types of cancer and improve the accuracy of prediction [7,8,9]
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