![[2602.22850] MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22850] MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction
Summary
The article presents MEDNA-DFM, a novel dual-view FiLM-MoE model designed for explainable DNA methylation prediction, highlighting its performance and biological insights.
Why It Matters
Understanding DNA methylation is crucial for epigenetics, and this model enhances predictive accuracy while providing explainable insights, bridging the gap between deep learning and biological interpretation. It could lead to new hypotheses in genetic research.
Key Takeaways
- MEDNA-DFM effectively captures conserved methylation patterns across species.
- The model's performance is driven by intrinsic motifs rather than phylogenetic proximity.
- New algorithms developed improve motif extraction reliability compared to previous studies.
- The research proposes a 'sequence-structure synergy' hypothesis validated through in silico mutagenesis.
- This work illustrates the potential of explainable deep learning in generating biological hypotheses.
Computer Science > Machine Learning arXiv:2602.22850 (cs) [Submitted on 26 Feb 2026] Title:MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction Authors:Yi He (1 and 4), Yina Cao (2), Jixiu Zhai (3 and 4), Di Wang (1 and 4), Junxiao Kong (4), Tianchi Lu (4 and 5) ((1) Cuiying Honors College, Lanzhou University, Lanzhou, Gansu, China, (2) School of Management, Lanzhou University, Lanzhou, Gansu, China, (3) Shanghai Innovation Institute, Shanghai, China, (4) School of Mathematics and Statistics, Lanzhou University, Lanzhou, Gansu, China, (5) Department of Computer Science, City University of Hong Kong, Kowloon, Hong Kong, China) View a PDF of the paper titled MEDNA-DFM: A Dual-View FiLM-MoE Model for Explainable DNA Methylation Prediction, by Yi He (1 and 4) and 27 other authors View PDF HTML (experimental) Abstract: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 c...
