Abstract: RNA secondary structure prediction is a core problem in bioinformatics,and recent advancements in deep learning have significantly propelled progress in this field.However,existing methods still face limitations in prediction accuracy and reliance on external prior models,which may compromise the robustness and generalization capabilities of these models.To address these issues,this paper proposes a Transformer-based model for RNA secondary structure prediction.The model designs dual feature encoding pathways,generating sequence features through linear embedding and one-hot encoding,and efficiently fuses these two feature representations using a cross-attention mechanism.During the feature extraction phase,the model employs an improved architecture combining Swin-Transformer and U-net(Swin-Unet) to achieve deep-level feature extraction,ultimately producing a pairing probability matrix for RNA secondary structures.Experimental results show that the proposed model achieves over 3% higher F1-scores than other models on multiple benchmark datasets without relying on prior information from external models.This study provides a novel solution for RNA structure prediction and highlights the promising potential of Transformer architectures in biological sequence analysis.

Key words: Prediction of RNA secondary structure, Deep learning, Swin-Transformer, Cross-attention, U-Net