MS/MS Spectrum Prediction for MHC-Associated Peptides with a Fine-Tuned Model
Bibliographic record
Abstract
To improve the quality of spectral library search, several MS/MS spectrum predictors have been developed in the last decades. After success in various fields, deep learning techniques are adopted by MS/MS spectrum predictors to increase the accuracy of predicted spectra. However, the quality and quantity of the training set are both required to train a deep learning model. Due to the less representation of MHC-associated peptides in most spectral libraries, current MS/MS spectrum predictors provide less accurate predicted spectra for MHC-associated peptides than their performance for other peptides. \n \nIn this thesis, we built several MHC-associated peptide spectral libraries for training and evaluation purposes. We selected PredFull as our base model and performed transfer learning with these MHC-associated peptide libraries, which are much smaller than com- mon tryptic spectral libraries. The result showed that the fine-tuned model outperformed the original model significantly when predicting MHC-associated peptides.
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How this classification was reachedexpand
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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".