Leveraging Nucleotide Dependencies for Improved mRNA Vaccine Degradation Prediction
Bibliographic record
Abstract
RNA sequence properties prediction is significant for understanding RNA function and its potential applications in medicine and biotechnology. In this study, we developed a novel Gated Recurrent Unit (GRU) deep learning model to predict mRNA vaccine degradation with improved accuracy over traditional machine learning methods and previously reported deep learning approaches. A notable contribution of our approach is the innovative method of feature engineering that accounts for dependencies between nucleotides by shifting the feature values. Our proposed GRU model outperformed XGBoost, Random Forest, and LightGBM models. The GRU Network showed a Mean Columnwise Root Mean Squared Error (MCRMSE) of 0.275 and 0.389 for the public and the private sets, respectively. Despite some limitations, our model provides a strong foundation for future work to refine and expand the capabilities of RNA sequence property prediction. The results of this study have significant implications for RNA research, potentially leading to advancements in understanding RNA function and the development of RNA-targeting therapeutics and diagnostic tools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".