Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory

Mining knowledge from gene expression data is a hot research topic and direction of bioinformatics. Gene selection and sample classification are significant research trends, due to the large amount of genes and small size of samples in gene expression data. Rough set theory has been successfully applied to gene selection, as it can select attributes without redundancy. To improve the interpretability of the selected genes, some researchers introduced biological knowledge. In this paper, we first employ neighborhood system to deal directly with the new information table formed by integrating gene expression data with biological knowledge, which can simultaneously present the information in multiple perspectives and do not weaken the information of individual gene for selection and classification. Then, we give a novel framework for gene selection and propose a significant gene selection method based on this framework by employing reduction algorithm in rough set theory. The proposed method is applied to the analysis of plant stress response. Experimental results on three data sets show that the proposed method is effective, as it can select significant gene subsets without redundancy and achieve high classification accuracy. Biological analysis for the results shows that the interpretability is well.