## 7 Conclusions  
The manuscript presents research results regarding the application of a recurrent neural network (RNN) for processing gene expression data. Two types of RNNs, LSTM and GRU, were investigated. An algorithm for optimizing the architecture and hyperparameter values of the RNN, which involves calculating both the accuracy of sample classification and the evaluation of the F1-score, the value of which allows for the assessment of the quality of sample distribution into respective classes, is proposed. An integral criterion of the F1-score based on the desirability method by [PERSON] was calculated to enhance the objectivity of decision making regarding model efficiency. Also proposed is a comprehensive data classification quality criterion using the relevant type of deep learning network, calculated as a weighted sum of partial quality criteria determined during the simulation process.  
Figure 12: The simulation results when applying the GRU RNN model.  
The modeling of various RNN architectures was carried out, and as a result, optimal hyperparameter values for each type of network were determined. The simulation results enabled the conclusion regarding the higher appeal of the single-layer GRU recurrent network with 75 neurons in the recurrent layer. A comparative analysis of convolutional and recurrent neural networks with optimal hyperparameters was also performed. It is shown 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%, and in other cases, 97.1%. In the first case, 954 out of 981 objects were correctly identified. In other cases, 952 were correctly identified. Although the convolutional neural network is more attractive in terms of the loss function value and training time, the GRU recurrent neural network is more appealing based on a set of criteria.  
The authors' further research perspective involves investigating the Bayesian optimization algorithm for optimizing the model's hyperparameter values with a comparative analysis of different types of models and methods for optimizing their parameters.  
Conceptualization, formal analysis, resources, writing--review and editing: S.B., I.K., and I.L.; methodology, software (R and Python programming), validation, statistical analysis and investigation, writing--original draft preparation: S.B.; results visualization: I.K. and I.L. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Ethical review and approval for this study were waived due to the type of study (retrospective study). The datasets from the freely available TCGA database were used during the simulation procedure implementation.  
Not applicable.  
Data available from the corresponding author upon request.  
We express our gratitude to the research team from the Center for Cancer Genomics at the National Cancer Institute, National Institutes of Health, and The Cancer Genome Atlas (TCGA) for providing the opportunity to download and utilize the gene expression datasets from patients investigated for various types of cancer diseases.  
The authors declare no conflict of interest.  
The following abbreviations are used in this manuscript:  
\begin{tabular}{l l} CNN & Convolutional neural network \\ RNN & Recurrent neural network \\ TCGA & The Cancer Genome Atlas \\ DL & Deep learning \\ LOAD & Late-onset Alzheimer's disease \\ RCO & Rider Chicken Optimization \\ CPM & Count Per Million \\ LSTM & Long Short-Term Memory \\ GRU & Gated Recurrent Unit \\ \end{tabular}