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Appl. Sci. 2018, 8(9), 1569; https://doi.org/10.3390/app8091569

Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

Faculty of Information Technology, Macau University of Science and Technology, Macau 999078, China
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Received: 8 August 2018 / Revised: 29 August 2018 / Accepted: 4 September 2018 / Published: 6 September 2018
(This article belongs to the Section Applied Biosciences and Bioengineering)
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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. View Full-Text
Keywords: gene selection; cancer classification; regularized logistic regression; L1/2 regularization gene selection; cancer classification; regularized logistic regression; L1/2 regularization
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Wu, S.; Jiang, H.; Shen, H.; Yang, Z. Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization. Appl. Sci. 2018, 8, 1569.

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