MeDiCNet: Integrating Multi-scale Dynamic Convolution and Enhanced Position-Aware Transformer for DNA Methylation...

DNA methylation is a covalent modification of cytosine and adenine bases that regulates gene expression and underlies diverse biological processes and diseases. Existing computational methods often rely on fixed-scale feature extraction or static positional encodings, limiting their ability to model both fine-grained sequence motifs and long-range dependencies across multiple methylation chemistries. We present MeDiCNet, a unified deep-learning framework that combines multi-scale dynamic convolution with enhanced positional attention to predict N6-methyladenine, 5-hydroxymethylcytosine and N4-methylcytosine sites. MeDiCNet encodes nucleotide identity, extracts local patterns via a dynamic convolution module, captures global context through a Transformer encoder into which positional information is injected via rotary and clipped relative position embeddings, and adaptively fuses these feature streams through a gated fusion module for final classification. We evaluated MeDiCNet on seventeen benchmark datasets spanning bacteria, fungi, plants and mammals. Compared with other methods, MeDiCNet improved overall accuracy (ACC) by up to 8.1% and Matthews correlation coefficient (MCC) by up to 0.10. For example, it achieved 94.82% accuracy on the F. vesca 6mA dataset and the area under the ROC (AUC) curve above 0.98 on the Mus musculus 5hmC dataset. Crucially, rigorous analysis confirms that MeDiCNet recovers biologically authentic motifs with high fidelity in an unsupervised manner, while requiring only about 16% of the parameters of comparable large language models. These results demonstrate MeDiCNet's ability to capture complex local and global sequence features, providing a robust, efficient, and interpretable tool for large-scale, cross-type epigenomic analysis.