## 6 Comparative Analysis of Convolutional and Recurrent Neural Networks with Optimal Hyperparameters  
The comparative analysis of the previously studied deep neural networks was performed by applying them to identical gene expression data. In this case, as in previous ones, the data were divided into three subsets: for network training, its validation during the training process, and testing the obtained model. The hyperparameter values of the convolutional neural network (CNN) were set considering our previous studies. In this instance, we applied the single-layer CNN, where number of filters = 32, kernel size = 3, Dense kernel = 64, maximal pooling = 3; the activation functions for convolutional layer, dense layer, and output layer were _sigmoid_, _selu_, and _softmax_, respectively. In Figures 9-11, diagrams illustrating changes in the accuracy of sample classification and the loss function values during the training of the investigated neural networks are depicted. The analysis of the obtained diagrams indicates the absence of network overfitting in all cases, since the character of changes in the respective criterion values when applying the training data subset and during model validation are consistent with each other. It should be noted that the training time for the convolutional neural network was 39 s, which was significantly less than that when using LSTM (185 s) and GRU (166 s) recurrent neural networks. In Figure 12, the diagrams of classification quality criteria based on gene expression data are depicted when applying different types of deep neural networks. Figure 8: Distribution diagrams of the classification comprehensive quality criterion when using GRU recurrent neural network. Analyzing the obtained results allows us to conclude that in terms of classification accuracy, calculated on the test data subset, the GRU neural network model is slightly better than the CNN and LSTM models. The classification accuracy when using the GRU network was 97.2%; in other cases, it was 97.1%. In the first case, 954 out of 981 objects were correctly identified. In other cases, 952 were correctly identified. In terms of the loss function value and training time, the convolutional neural network is more appealing. The distribution pattern of F1-score values also indicates a small disparity in the sample identification results when distributed into respective classes. The analysis of the comprehensive quality criterion values confirms the conclusion regarding the greater appeal of the GRU recurrent neural network based on a set of criteria. This fact affirms the adequacy of the proposed method for evaluating the quality of the neural network according to a set of quality criteria, enhancing the objectivity of forming the vector of optimal hyperparameters during the model tuning process. Figure 11: Diagrams of changes in accuracy values and the loss function, calculated on the training subset and during model validation when applying the GRU RNN model. Figure 10: Diagrams of changes in accuracy values and the loss function, calculated on the training subset and during model validation when applying the LSTM RNN model. Figure 9: Diagrams of changes in accuracy values and the loss function, calculated on the training subset and during model validation when applying the CNN model. A comparative analysis of simulation results revealed a preference for the GRU recurrent neural network over both the CNN and LSTM RNN in terms of classification quality criteria for gene expression data processing. However, when evaluating based on loss function values, CNN-based models were superior. While CNNs are widely appreciated for their capacity to automatically learn spatial features from gene expression data, effectively discern biologically relevant patterns, and handle high-dimensional datasets without significant feature engineering, they are also known to consistently improve performance as they encounter more data, underscoring their value in genomics.