DeepRNAac4C: a hybrid deep learning framework for RNA N4-acetylcytidine site prediction.

Abstract

RNA N4-acetylcytidine (ac4C) is a crucial chemical modification involved in various biological processes, influencing RNA properties and functions. Accurate prediction of RNA ac4C sites is essential for understanding the roles of RNA molecules in gene expression and cellular regulation. While existing methods have made progress in ac4C site prediction, they still struggle with limited accuracy and generalization. To address these challenges, we propose DeepRNAac4C, a deep learning framework for RNA ac4C sites prediction. DeepRNAac4C integrates residual neural networks, convolutional neural networks (CNN), bidirectional long short-term memory networks (BiLSTM), and bidirectional gated recurrent units (BiGRU) to effectively capture both local and global sequence features. We extensively evaluated DeepRNAac4C against state-of-the-art methods using 10-fold cross-validation and independent tests. The results show that DeepRNAac4C outperforms existing approaches, achieving an accuracy of 0.8410. The proposed DeepRNAac4C improves predictive accuracy and model robustness, providing an effective tool for identifying RNA ac4C sites and deepening our understanding of RNA modifications and their functional roles in biological systems.