Gene Expression Pattern Recognition Algorithm Based on Deep Learning

With the rapid development of bioinformatics, the analysis of gene expression data has become crucial for understanding life processes, disease mechanisms, and drug development. For binary classification tasks, the performance of most traditional machine learning models is highly dependent on the quality of features, which often requires researchers to have a deep professional background, and artificially constructed feature vectors may lose the original information in protein sequences. This study aims to use deep learning models to extract high-order features from gene expression data, in order to solve the problem of gene expression pattern recognition. Firstly, by analyzing the limitations of traditional biological methods in gene recognition, a new approach combining recurrent neural networks (RNNs) and Transformer models is proposed. RNN can effectively capture temporal dependencies in sequence data, while Transformer, through its multi head self-attention mechanism, can more effectively process long sequence data and capture complex dependencies in gene expression sequences. Through experiments on multiple publicly available gene expression datasets, it has been demonstrated that the Transformer model outperforms the RNN model, demonstrating its superiority in feature extraction. Then, in response to the problem of low efficiency in processing long sequences with the Transformer model, this study further proposes the CNN Transformer model. The model first uses one-dimensional convolutional layers to extract local features from gene expression data, and then uses Transformer layers to capture long-range dependencies between these features. The experimental results show that the CNN Transformer model exhibits higher balanced accuracy on multiple independent test sets, demonstrating the effectiveness of the model structure. Finally, this article discusses the potential application value of this method in fields such as personalized medicine and early disease diagnosis, providing direction for future research. In summary, this study not only provides a new solution for identifying gene expression patterns, but also opens up new avenues for a deeper understanding of fundamental issues in life sciences.