New Hybrid Gene Selection-Sample Classification Method in Microarray Data

Abstract

The gene expression dataset generated by DNA microarray technology contains expression profiles of huge quantities of genes for very small samples. Among these genes, a very small number of genes are informative for cancer sample identification and classification. Informative genes finding is an essential task of microarray gene expression data analysis. Here, a new hybrid gene selection-sample classification model (NHGSSC) is proposed for selection of relevant genes and classification of cancer samples. The NHGSSC performs two tasks-gene selection and sample classification. For gene selection, a new hybrid single filter and α-depth limited best first search based single wrapper method (SFα-BFSSW) is proposed. From these subsets, highly informative genes are selected by counting frequency of occurrence (FO) of every gene. Then SFα-BFSSW method-based ensemble classifier (SFα-BFSSWEC) is built by combining the classifiers created for the selected gene subsets. Experimental results demonstrate the superiority of the NHGSSC to other existing models.