Sparse logistic regression with a L-1/2 penalty for gene selection in cancer classification
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AbstractBackground: 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.
All Author(s) ListLiang Y, Liu C, Luan XZ, Leung KS, Chan TM, Xu ZB, Zhang H
Journal nameBMC Bioinformatics
Volume Number14
LanguagesEnglish-United Kingdom
KeywordsCancer classification; Gene selection; Sparse logistic regression
Web of Science Subject CategoriesBiochemical Research Methods; BIOCHEMICAL RESEARCH METHODS; Biochemistry & Molecular Biology; Biotechnology & Applied Microbiology; BIOTECHNOLOGY & APPLIED MICROBIOLOGY; Mathematical & Computational Biology; MATHEMATICAL & COMPUTATIONAL BIOLOGY

Last updated on 2020-20-10 at 01:17