Sparse logistic regression with a L-1/2 penalty for gene selection in cancer classification
Publication in refereed journal


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
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