## 4 Experiment, Results and Discussion
### Experimental Data
Figure 5d shows a chart of the balance criterion, the analysis of which allows us to allocate the ranges of the parameter variation, which achieves the maximal values of the balance criterion on the one hand and stable clustering on the other hand. A more careful analysis of the obtained results showed that the balance criterion reaches its maximum value (0.985) at the fourth step of this procedure implementation. In this case, the low value of the internal criteria also confirms the fact that at this step, the nature of the GEPs distribution in the clusters corresponds to the optimal cluster structure. It should be noted that in all cases (at each iterative step), the data were divided into two clusters. The algorithm stopped when repeating the configuration of the cluster structures on two consecutive iterations. Thus, the value \(\alpha_{s}=0.0007\) was chosen as an optimal one for further simulation stages. At the second stage of the model's implementation, the SOTA clustering algorithm with a determined parameter was applied to the full set of GEPs (10,000). The result of this step implementation formed two clusters: the first contained 6020 profiles, and the second contained 3980 profiles. The classification procedure was performed using a one-dimensional two-layer convolutional neural network (CNN), the efficiency and robustness of which for gene expression data classification was proven in [28]. For the correct application of the CNN, the first cluster was supplemented with profiles with zero expression to obtain a total of 6050 profiles. In this case, filters (\(50\times 121\)) and (\(25\times 242\)) were applied on the first and second convolutional layers, respectively. The second cluster was supplemented to obtain a total of 4000 profiles, while the filters had dimensions (\(50\times 80\)) and (\(25\times 160\)) on the first and the second layers. The simulation results regarding the application of CNN to gene expression data contained in the formed clusters are shown in Figures 6 and 7. Figure 6: Charts of objects classification accuracy and the loss function value during the network training on the data in the first cluster. Figure 7: Charts of objects classification accuracy and the loss function value during the network training on the data in the second cluster. The accuracy of object classification using the corresponding test data subsets was 95% for the first cluster and 97% for the objects of the second one, while from 39 objects, 37 and 38 were correctly identified in the first and second cases, respectively. At the next stage, the SOTA algorithm was applied stepwise to the GEPs contained in the appropriate clusters, followed by the classification of the objects based on the data obtained clusters by applying CNN with adapted filters. The simulation results are presented in Table 3.  
Figure 8 shows a dendrogram of the GEP distribution into clusters with values of the investigated object classification accuracy. The analysis of the obtained results allows us to conclude that the joint application of the SOTA clustering algorithm and the CNN allows us to identify clusters of GEP that can allow us to identify the objects that contain the value of gene expressions that are localized in the corresponding clusters as attributes with high accuracy. As can be seen, in the first