###### Abstract  
The importance of gene expression data processing in solving the classification task is determined by its ability to discern intricate patterns and relationships within genetic information, enabling the precise categorization and understanding of various gene expression profiles and their consequential impacts on biological processes and traits. In this study, we investigated various architectures and types of recurrent neural networks focusing on gene expression data. The effectiveness of the appropriate model was evaluated using various classification quality criteria based on type 1 and type 2 errors. Moreover, we calculated the integrated F1-score index using the Harrington desirability method, the value of which allowed us to improve the objectivity of the decision making when model effectiveness was evaluated. The final decision regarding model effectiveness was made based on a comprehensive classification quality criterion, which was calculated as the weighted sum of classification accuracy, integrated F1-score index, and loss function values. The simulation results show higher appeal of a single-layer GRU recurrent network with 75 neurons in the recurrent layer. We also compared convolutional and recurrent neural networks on gene expression data classification. Although convolutional neural networks showcase benefits in terms of loss function value and training time, a comparative analysis revealed 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 objects were correctly identified. g 2023  
deep learning models can autonomously learn feature representations from raw data, eliminating the need for manual feature extraction, thereby reducing potential biases introduced during this process. Additionally, the models, when adequately trained, demonstrate an impressive ability to minimize error and optimize classification tasks, which is vital to ensuring reliable predictive performance. The applicability of deep learning models, such as autoencoders and variational autoencoders, also facilitates effective dimensionality reduction in gene expression data, aiding in mitigating the curse of dimensionality [2]. Moreover, in an era where precision medicine is pivotal, the application of deep learning models to classifying gene expression data is paramount to identifying specific disease subtypes and enabling targeted therapeutic approaches. The scalability of deep learning models also affords them the flexibility to handle large genomic datasets, which is often a requisite in bioinformatics research. Furthermore, the adaptive nature of deep learning models allows them to evolve with increasing data and complexity, ensuring that they remain relevant and applicable in the dynamically progressing field of genomics. Lastly, the amalgamation of deep learning with existing biological knowledge can potentially unearth novel insights and drive advancements in understanding gene expression mechanisms and their implications in various biological phenomena and diseases. These facts indicate the actuality of the research in this subject area. Gene expression data processing in molecular biology uses both experimental and computational methods to analyze gene activity. The widely used qRT-PCR method quantifies specific gene expression levels and is excellent for validating high-throughput results [3]. Microarrays, which analyze thousands of genes simultaneously, have long been standard but are gradually being surpassed by RNA sequencing (RNA-Seq) due to its higher resolution and capability to detect novel transcripts. Drop-seq, a newer method, combines RNA-seq with microfluidics to study individual-cell gene expression [4].