Sparse logistic regression with a L-1/2 penalty for gene selection in cancer classification
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摘要Background: Microarray technology is widely used in cancer diagnosis. Successfully identifying gene biomarkers will significantly help to classify different cancer types and improve the prediction accuracy. The regularization approach is one of the effective methods for gene selection in microarray data, which generally contain a large number of genes and have a small number of samples. In recent years, various approaches have been developed for gene selection of microarray data. Generally, they are divided into three categories: filter, wrapper and embedded methods. Regularization methods are an important embedded technique and perform both continuous shrinkage and automatic gene selection simultaneously. Recently, there is growing interest in applying the regularization techniques in gene selection. The popular regularization technique is Lasso (L-1), and many L-1 type regularization terms have been proposed in the recent years. Theoretically, the Lq type regularization with the lower value of q would lead to better solutions with more sparsity. Moreover, the L-1/2 regularization can be taken as a representative of Lq (0 < q < 1) regularizations and has been demonstrated many attractive properties.
著者Liang Y, Liu C, Luan XZ, Leung KS, Chan TM, Xu ZB, Zhang H
期刊名稱BMC Bioinformatics
出版年份2013
月份6
日期19
卷號14
出版社BIOMED CENTRAL LTD
國際標準期刊號1471-2105
語言英式英語
關鍵詞Cancer classification; Gene selection; Sparse logistic regression
Web of Science 學科類別Biochemical Research Methods; BIOCHEMICAL RESEARCH METHODS; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; BIOTECHNOLOGY & APPLIED MICROBIOLOGY; Mathematical & Computational Biology; MATHEMATICAL & COMPUTATIONAL BIOLOGY

上次更新時間 2020-20-10 於 01:17