Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration

Abstract

Abstract Background RNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in computational biology. This paper introduces Deep-m5U, a robust predictor designed to enhance the prediction of m5U modifications. The proposed method, named Deep-m5U, utilizes a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, a Shapley Additive exPlanations (SHAP) algorithm for discriminant feature selection, and a deep neural network (DNN) as the classifier. Results The model was evaluated using two benchmark datasets, i.e., Full Transcript and Mature mRNA. Deep-m5U achieved overall accuracies of 91.47% and 95.86% for the Full Transcript and Mature mRNA datasets with 10-fold cross-validation, and for independent samples, the model attained 92.94% and 95.17% accuracy. Conclusion Compared to existing models, Deep-m5U showed approximately 5.23% and 3.73% higher accuracy on the training data and 3.95% and 3.26% higher accuracy on independent samples for the Full Transcript and Mature mRNA datasets, respectively. The reliability and effectiveness of Deep-m5U make it a valuable tool for scientists and a potential asset in pharmaceutical design and research.

Keywords

• RNA 5-methyluridine
• PseKNC
• Deep learning
• Sequence-derived features
• SHAP