PROJECT TITLE :
Gene Selection Integrated with Biological Knowledge for Plant Stress Response Using Neighborhood System and Rough Set Theory
Mining data from gene expression data could be a hot analysis topic and direction of bioinformatics. Gene choice and sample classification are significant analysis trends, thanks to the large quantity of genes and small size of samples in gene expression data. Rough set theory has been successfully applied to gene choice, as it can select attributes without redundancy. To enhance the interpretability of the chosen genes, some researchers introduced biological information. During this paper, we have a tendency to initial employ neighborhood system to deal directly with the new info table formed by integrating gene expression knowledge with biological information, that can simultaneously present the information in multiple perspectives and do not weaken the knowledge of individual gene for choice and classification. Then, we tend to offer a completely unique framework for gene selection and propose a vital gene selection technique based on this framework by using 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, because it can select significant gene subsets while not redundancy and achieve high classification accuracy. Biological analysis for the results shows that the interpretability is well.
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