关键词: 肿瘤基因表达谱, Lasso, 特征选择, 支持向量机

Abstract: With the advent of DNA microarray technology, a large number of open-access tumor gene expression datasets are searchable online and can be downloaded. Informative gene selection and tumor subtype classification have been becoming one of primary research fields in Bioinformatics. This paper proposes K-split Lasso (least absolute shrinkage and selection operator) method for gene selection, whose main idea is to divide the feature sets into K parts, and then select the genes from each feature subset using Lasso, finally merge the selected genes into one feature subset to get the informative genes. Using the support vector machine as classification tool, the experimental results indicate that K-split Lasso reduces data redundancy, improves sample classification accuracy, and has good stability. In addition, K-split Lasso overcomes the large computation and overfitting problems due to the decrease of dimension. K-split Lasso is an effective method for gene selection of tumor.

Key words: tumor gene expression profiles, Lasso, feature selection, support vector machine