###### Abstract
After experimental data collection, bioinformatics tools are essential for tasks like normalization and differential expression analysis. DESeq2 and edgeR are examples of popular software for such tasks. CNNs have emerged as a pivotal tool for classifying gene expression data, thanks to their autonomous feature-learning capability, which curtails the need for manual intervention in high-dimensional genomic data extraction [5; 6; 7]. By recognizing patterns effectively, they capture both local and global spatial hierarchies of gene expression profiles, a key aspect in identifying complex biological states. The inherent mechanism of dimensionality reduction through pooling layers in CNNs substantially alleviates computational challenges, proving particularly instrumental when dealing with voluminous gene expression datasets. Moreover, CNNs maintain and interpret spatial relationships within the data, which is essential to understanding the spatial configuration of genomic sequences. Importantly, CNNs facilitate transfer learning, enabling the adaptation of pre-trained models to specific gene expression datasets, a vital attribute in scenarios with limited labeled data. They exhibit commendable robustness against data noise, ensuring predictive accuracy despite potential experimental variations in gene expression data. CNNs, while complex, can be rendered interpretable using specific visualization techniques, revealing insightful features and enhancing model transparency. Ultimately, they recognize features at varied scales and ensure effective generalization through strategic model design, adeptly applying learned patterns to novel, unseen gene expression data, thereby holding substantial promise for practical applications in both biomedical research and diagnostics. However, it is important to highlight that a significant drawback of CNNs, impeding their efficient utilization, is the substantial set of hyperparameters, the optimal values that define the network's architecture. Typically, values for these hyperparameters are determined using either a grid search algorithm or the Bayesian optimization method [8; 9]. Nevertheless, this process demands notable expenditure of time and computational resources. Recurrent neural networks (RNNs) have specific advantages over convolutional neural networks (CNNs) when it comes to processing gene expression data, especially concerning sequence prediction and temporal dynamics understanding [10]. Firstly, RNNs are inherently adept at handling sequential data, which is crucial to analyzing gene expression time series where temporal dependencies exist. Secondly, they are capable of maintaining memory from previous inputs in their internal state, enabling them to capture dynamic temporal behaviors in gene expression profiles, unlike CNNs, which typically treat input features as independent. Thirdly, the gated units in advanced RNNs, like LSTM and GRU, can selectively remember or forget information, which is essential for learning long-term dependencies observed in certain biological processes. Fourthly, RNNs can model sequential data of varied lengths without the necessity to fix the input size, providing flexibility in dealing with gene expression profiles from varied experimental setups. Fifthly, they naturally support bi-directional input processing, which can be beneficial for exploring gene expression profiles that may have bi-directional influences. Lastly, RNNs may facilitate more intuitive insights into gene expression pathways and cascades due to their sequential processing nature, potentially offering more biologically relevant interpretations compared with the hierarchical feature learning in CNNs. However, it is noteworthy that RNNs also come with challenges, such as susceptibility to vanishing and exploding gradient problems in training, which need careful consideration during model development and application in the bioinformatics domain.