A stable gene selection in microarray data analysis

Background: Microarray data analysis is notorious for involving a huge number of genes compared to a relatively small number of samples. Gene selection is to detect the most significantly differentially expressed genes under different conditions, and it has been a central research focus. In general, a better gene selection method can improve the performance of classification significantly. One of the difficulties in gene selection is that the numbers of samples under different conditions vary a lot. Results: Two novel gene selection methods are proposed in this paper, which are not affected by the unbalanced sample class sizes and do not assume any explicit statistical model on the gene expression values. They were evaluated on eight publicly available microarray datasets, using leaveone-out cross-validation and 5-fold cross-validation. The performance is measured by the classification accuracies using the top ranked genes based on the training datasets. Conclusion: The experimental r...

• Type: Journal
• Year: 2006
• Where: BMCBI
• Authors: Kun Yang, Zhipeng Cai, Jianzhong Li, Guohui Lin
• Keywords: BMCBI 2006, Expressed Genes, Expression Levels, Gene Selection