Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification.

ABSTRACT:

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

SUBMITTER: Liang Y 

PROVIDER: S-EPMC3718705 | biostudies-literature | 2013 Jun

REPOSITORIES: biostudies-literature