m5U-HybridNet: Integrating an RNA Foundation Model with CNN Features for Accurate Prediction of 5-Methyluridine Modification Sites

The 5-methyluridine (m5U) modification in RNA is vital for numerous biological processes, making its precise identification a key focus in computational biology. However, traditional wet-lab detection methods are cumbersome and time-consuming, whereas existing machine learning and deep learning computational prediction models still have room for improvement. Consequently, this study introduces m5U-HybridNet, an innovative framework that strategically integrates an RNA foundation model (RNA-FM) for deep semantic feature extraction with convolutional neural network-derived characteristics, attaining unparalleled success in identifying RNA m5U modification sites. Simultaneously, when compared with other existing models across different cell types and experimental techniques, it exhibits outstanding generalization capabilities. The m5U-HybridNet web server, accessible at http://www.bioai-lab.com/m5U, offers an effective and reliable platform for predicting RNA modification sites. It not only implies the diverse potential applications of pretrained models in the analysis of biological sequences but also enhances the application of data-driven machine intelligence in the realm of molecular biophysics principles.