KD_MultiSucc: incorporating multi-teacher knowledge distillation and word embeddings for cross-species prediction of protein succinylation sites
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 succinylation is a vital post-translational modification (PTM) that involves the covalent attachment of a succinyl group (-CO-CH2-CH2-CO-) to the lysine residue of a protein molecule. The mechanism underlying the succinylation process plays a critical role in regulating protein structure, stability, and function, contributing to various biological processes, including metabolism, gene expression, and signal transduction. Succinylation has also been associated with numerous diseases, such as cancer, neurodegenerative disorders, and metabolic syndromes. Due to its important roles, the accurate prediction of succinylation sites is essential for a comprehensive understanding of the mechanisms underlying succinylation. Although research on the identification of protein succinylation sites has been increasing, experimental methods remain time-consuming and costly, underscoring the need for efficient computational approaches. In this study, we present KD_MultiSucc, a model for cross-species prediction of succinylation sites using Multi-Teacher Knowledge Distillation and Word Embedding. The proposed method leverages the strengths of both Knowledge Distillation and Word Embedding techniques to reduce computational complexity while maintaining high accuracy in predicting protein succinylation sites across species. Experimental results demonstrate that the proposed predictor outperforms existing predictors, providing a valuable contribution to PTM research and biomedical applications. To assist readers and researchers, the codes and resources related to this work have been made freely accessible on GitHub at https://github.com/nuinvtnu/KD_MultiSucc/.
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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 | 0.001 | 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