Integrating Graph Signal Processing with Graph Convolutional Networks for N - and O-Glycosylation Site Prediction
Why this work is in the frame
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Bibliographic record
Abstract
This paper introduces a new integrated framework that unites Graph Signal Processing methods with Graph Convolutional Networks for N - and O-glycosylation site prediction in proteins. Our approach builds dual graph representations from residue-level embeddings based on both sequential and proximity-based relations to model local and distant dependencies. Spectral transforms viz., Graph Fourier, Cosine, and Scattering Transforms are used to preprocess the node features before passing them to parallel Graph Convolutional branches. Saliency analysis also proves evidence towards significant residues impacting predictions. In the case of N -linked glycosylation, our best-performing model achieves MCC of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 5. 9 6 \%}$</tex> and an F1 score of <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{7 8. 8 9 \%}$</tex>, while in the case of Olinked glycosylation, it achieves MCC of 52.21% and an F1 score of 57.71%. Experimental evidence demonstrates that the new method performs competitively across a range of measures, an indication of its usefulness in enhancing glycosylation site prediction.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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