UniMethylNet: A Universal DNA Methylation Site Prediction Network Integrating a Neural Network and an Attention Mechanism
Bibliographic record
Abstract
DNA methylation plays a crucial role in biological processes. However, existing prediction methods often suffer from limited generalization ability due to the scale and diversity constraints of the training samples, as well as insufficient recognition of significant interspecies differences in methylation patterns. To address these challenges, this study proposes a novel methylation site prediction model, namely, UniMethylNet, for robust identification of different methylation types (4mC, 5hmC, and 6 mA) across 12 species. UniMethylNet incorporates a Position Linear Layer to precisely capture local patterns, while utilizing a Bidirectional Long Short-Term Memory network to model long-term dependencies. UniMethylNet also employs a Channel-Spatial Dual Attention module for adaptive feature weighting and multiscale focusing, enabling one to effectively extract methylation-related features. Experimental results on 20 public data sets demonstrate that UniMethylNet achieves a mean accuracy of 87.78% and a mean area under the receiver operating characteristic curve of 93.01%, significantly surpassing existing models and exhibiting superior cross-species and cross-type generalization. Overall, UniMethylNet provides a powerful tool for DNA methylation site prediction, offering a quantitative approach for in-depth exploration of the conservation and specificity of epigenetic regulation by capturing the underlying conserved sequence motifs across diverse biological contexts.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".