Attention-Based RNA Secondary Structure Prediction

RNA is a molecule composed of ribonucleotides and plays a crucial role in biological activities. The computational prediction of RNA secondary structures has been a long-standing issue in computational biology. Traditional methods for this problem are based on free energy minimization, but the performance of these methods has reached an upper limit. In recent years, various deep learning-based methods have been proposed, but these models are still primitive and prone to overfitting, resulting in poor performance across RNA families. In this paper, we propose two methods, AttnUFold and TransUFold, which utilize the attention mechanism to enhance the model's learning ability for the global features of RNA sequences. Additionally, we modify the loss function to cope with sample distribution imbalances and attempt to introduce relevant constraints for RNA folding. Compared with the baseline, the two models have brought improvements in both within-and cross-family tasks. AttnUFold achieved a high F1 score of 0.852 on the ArchiveII dataset, surpassing all traditional and most deep learning methods.