K-SNOpred: Identification of protein S-nitrosylation sites through word embedding features and machine learning
Bibliographic record
Abstract
Protein S-nitrosylation (SNO) is a process involving the covalent modification of cysteine residues by nitric oxide (NO) and its derivatives. Numerous studies have demonstrated that SNO is significantly involved in cell function and pathophysiology. The identification of SNO sites is significant in clarifying their function in cellular physiology, disease processes, and potential treatment strategies, rendering it of paramount importance in medical science. This study developed a machine learning (ML) model named "K-SNOpred" and found notable performance in identifying SNO sites using the Latent Semantic Analysis (LSA) feature embedding system. After collecting dbSNO and RecSNO datasets from the literature search, we applied three feature embedding systems: Doc2vec, FastText, and LSA on each dataset. The study employed various ML models and assessed their performance using multiple evaluation metrics through independent testing and 10-fold cross-validation. The evaluation's outcomes demonstrate that the proposed model achieved an accuracy of 87.56 % and an AUC score of 95.06 %, outperforming existing state-of-the-art (SOTA) models by nearly 10 % in accuracy and 6 % in AUC. Furthermore, the model demonstrated balanced sensitivity and specificity, indicating its ability to detect both positive and negative SNO sites accurately. The outstanding performance of the K-SNOpred model demonstrates its high potential for clinical use and its applicability in the biotechnology field.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".