DHUpredET: A comparative computational approach for identification of dihydrouridine modification sites in RNA sequence
Bibliographic record
Abstract
Laboratory-based detection of D sites is laborious and expensive. In this study, we developed effective machine learning models employing efficient feature encoding methods to identify D sites. Initially, we explored various state-of-the-art feature encoding approaches and 30 machine learning techniques for each and selected the top eight models based on their independent testing and cross-validation outcomes. Finally, we developed DHUpredET using the extra tree classifier methods for predicting DHU sites. The DHUpredET model demonstrated balanced performance across all evaluation criteria, outperforming state-of-the-art models by 8% and 14% in terms of accuracy and sensitivity, respectively, on an independent test set. Further analysis revealed that the model achieved higher accuracy with position-specific two nucleotide (PS2) features, leading us to conclude that PS2 features are the best suited for the DHUpredET model. Therefore, our proposed model emerges as the most favorite choice for predicting D sites. In addition, we conducted an in-depth analysis of local features and identified a particularly significant attribute with a feature score of 0.035 for PS2_299 attributes. This tool holds immense promise as an advantageous instrument for accelerating the discovery of D modification sites, which contributes too many targeting therapeutic and understanding RNA structure. • Executed an in-depth analysis employing various machine learning techniques. • Uncovered the most informative features and optimal machine-learning algorithms for predicting D sites in RNA sequences. • Proposed DHUpredET, a machine learning-based method than can significantly outperform previous studies found in the literature. • Exceled at leveraging position-specific two nucleotides (PS2) features with appropriate execution. • Identified the positive class with greater than 85% and specified the negative class with more than 82% accuracy, which produces a balance outcome.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".