DT-m6A: A DenseNet–Transformer Hybrid Framework for Accurate Prediction of m6A Modification Sites across Diverse Cell Lines and Tissues

DT-m6A surpassed the state-of-the-art method MST-m6A by 0.63% in average accuracy (<i>p</i> = 0.0023) and 1.4% in mean MCC (<i>p</i> = 0.0012) across 11 independent test sets. Although its performance on the CD8T and MOLM13 cell lines was comparable to MST-m6A, DT-m6A consistently achieved superior results across all other cell lines and tissues. Overall, DT-m6A effectively captures both local patterns and global dependencies in RNA sequences, improving prediction performance across diverse biological contexts.