The Application of AI to Gene Classification

Gene classification is important in disease identification, early diagnosis, and precise treatment. At the same time, traditional manual methods are limited by their time-consuming and low accuracy, and algorithms based on deep learning show outstanding potential. In this paper, the main research progress in the field of gene classification is reviewed, and the application characteristics and performance of deep learning models such as multi-layer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN) are analyzed. In particular, the advantages of sci-kit-learn, Bidirectional Encoder Representations from Transformers (BERT), and DNASimCLR frameworks in gene classification are emphasized, and the importance of data preprocessing, feature extraction, and data set construction is discussed. At the same time, the limitations of current techniques are pointed out, including low training efficiency, sensitivity to outliers, and strong data dependence, and the possible direction of improving model performance through regularization techniques, pre-training models, data diversity enhancement, and other strategies are proposed. The research in this paper provides a reference for the optimization and popularization of gene classification technology and looks forward to its broad application in disease diagnosis and bioinformatics.