## 4 Experiment, Results and Discussion  
### Experimental Data  
We used the gene expression dataset GSE19188 [29] from the freely available database GEO (the Geo Expression Omnibus) [30] as the experimental data. This dataset contains the gene expression values for 156 patients examined for lung cancer. Of them, 65 patients were identified as healthy and 91 were identified with lung cancer tumors. Initially, this data contained 54,675 genes. Formation of the Subsets of GEPs Considering the Significance Level Using the Harrington Desirability Method  
Figure 4 shows the block charts of private desirabilities and the general desirability index, which determine the gene expression profiles' significance level and were calculated for the maximum values of gene expressions for all samples, variance, and Shannon entropy. An analysis of the resulting charts allows us to conclude based on the values of private desirability and the value of the general desirability index, most profiles (54,087 from 54,675) are identified as profiles with a very low significance level. In this instance, 15 genes were identified as having a very high significance level, 38 were identified as genes with a high significance level, and 142 and 393 profiles were identified as genes with medium and low significance levels, respectively. However, it should be noted that informative genes are not mutually expressed. This fact is confirmed by the classification results of samples containing, as the attributes, gene expression values allocated in appropriate subsets (Table 1). Figure 4: Box plots of private desirabilities and the general desirability index, which determines the gene expression profiles’ significance level. Indeed, at first glance, the presented results are not logical. However, it should be noted that this fact only shows that the application of the proposed method can allow us only to divide the initial set of gene expression profiles into informative and non-informative ones according to the appropriate quantitative criteria. Moreover, the number of informative genes is determined by the threshold value of the general desirability index, which can vary depending on the nature of the experimental data distribution. Within the framework of the current research, considering the particularities of the GEPs' general desirability index (GI) values distribution, the boundary value of the GI dividing the gene expression profiles into informative and non-informative ones was chosen at the level of 0.04. In this case, 10,000 gene expression profiles were selected. To assess the model adequacy by applying a classifier, this set of genes was divided into three subsets: 0.04 \(\leq GI<\) 0.2: medium significance (9162 genes); 0.2 \(\leq GI<\) 0.37: high significance (513 genes); \(GI\geq\) 0.37: very high significance (332 genes). The classifier was applied to the full set of gene expression data (10,000 profiles) and to the gene expression profiles in the formed subsets. The classification results are presented in Table 2.  
As it can be seen, in all cases, the value of the classification quality criteria is quite high but not the maximum. Moreover, the classification results of objects containing a large number of gene expression values as attributes (10,000 and 9162) are the same. This fact can be explained by the presence of a large number of differentially expressed gene expression profiles that can be identified as noise. The highest quality of the samples classification is achieved when using 513 genes of high significance level, which can be explained by a significantly smaller number of genes on the one hand and the presence of a larger number of genes designated as informative by the applied criteria on the other hand. The presented research demonstrates the advisability of using the proposed technique for preprocessing gene expression data to reasonably remove uninformative genes based on measures of GEPs significance.