MHCherryPan: a novel pan-specific model for binding affinity prediction of class I HLA-peptide
Why this work is in the frame
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Bibliographic record
Abstract
The Human Leukocyte Antigen (HLA) system or complex plays an irreplaceable role in regulating the humans' immune system. Accurate prediction of peptide binding with HLA can efficiently promote to identify those neoantigens, which potentially make a great change in immune drug development. HLA is one of the most polymorphic genetic systems in humans, and thousands of HLA allelic versions exist (Choo, 2007). Owing the high polymorphism of HLA complex, it is still pretty difficult to accurately predict the binding affinity. In this paper, we proposed a novel algorithm which combined convolutional neural network and long short-term memory to solve this problem. Our model has been tested with the experimental benchmark from IEDB and shows the state-of-the-art performance compared with other currently popular algorithms.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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