K-SNOpred: Identification of protein S-nitrosylation sites through word embedding features and machine learning
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it