MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction
This work provides a powerful and explainable tool for accurate DNA methylation prediction, which is crucial for understanding epigenetic regulation, and generates biological hypotheses for researchers in epigenetics.
The paper introduces MEDNA-DFM, a deep learning model for DNA methylation prediction that achieves robust distinction across diverse species. It also develops signal purification algorithms that extract motifs with significantly higher reliability than previous methods, leading to a "sequence-structure synergy" hypothesis for Drosophila 6mA.
Accurate computational identification of DNA methylation is essential for understanding epigenetic regulation. Although deep learning excels in this binary classification task, its "black-box" nature impedes biological insight. We address this by introducing a high-performance model MEDNA-DFM, alongside mechanism-inspired signal purification algorithms. Our investigation demonstrates that MEDNA-DFM effectively captures conserved methylation patterns, achieving robust distinction across diverse species. Validation on external independent datasets confirms that the model's generalization is driven by conserved intrinsic motifs (e.g., GC content) rather than phylogenetic proximity. Furthermore, applying our developed algorithms extracted motifs with significantly higher reliability than prior studies. Finally, empirical evidence from a Drosophila 6mA case study prompted us to propose a "sequence-structure synergy" hypothesis, suggesting that the GAGG core motif and an upstream A-tract element function cooperatively. We further validated this hypothesis via in silico mutagenesis, confirming that the ablation of either or both elements significantly degrades the model's recognition capabilities. This work provides a powerful tool for methylation prediction and demonstrates how explainable deep learning can drive both methodological innovation and the generation of biological hypotheses.