New gene selection method for multiclass tumor classification by class centroid

In the analysis of gene expression profiles, the selection of genetic markers and precise diagnosis of cancer type are crucial for successful treatment. The selection of discriminatory genes is critical to improve the accuracy and decrease computational complexity and cost in microarray analysis. In this paper, we developed a new statistical parameter, the suitability score to filter genes which only utilize sample distances from the class centroid. The filtered genes are employed in the nearest centroid classification to classify cancer. To evaluate the performance of the new statistical parameter, the proposed approach is applied to three publicly available microarray datasets. In this paper we demonstrate that the proposed gene selection method is steady in handling classification tasks and is a useful tool for gene selection and mining high dimension data.