A review of gene selection tools in classifying cancer microarray data

Abstract

Background: The measurement of expression levels of many genes through a single experiment is now possible due to the development of DNA microarray technology. However, many computational methods are having difficulties in selecting a small subset of genes because there are a few samples compared to the huge number of genes, irrelevant genes and noisy genes. Objective: This paper presents a review of existing tools for gene selection divided into four different categories. Method: In addition, most studies focus on selecting a small subset without analysing the genes’ functional and biological characteristics. Many researchers are continuously seeking solutions to this problem. Microarray data analysis has been successfully applied to gene selection algorithms in a different development environment. Results: Many different tools have been generated for gene selection in classifying microarray data. Conclusion: A suitable and user-friendly tool for users and biomedical researchers should be developed to avoid selection biases and allow analysis of multiple solutions.