SeqDA-HLA: Language Model and Dual Attention-Based Network to Predict Peptide-HLA Class I Binding
Bibliographic record
Abstract
Accurate prediction of peptide-HLA class I binding is crucial for immunotherapy and vaccine development, but existing methods often struggle to capture the intricate biological relationships between peptides and diverse HLA alleles. Here, we introduce SeqDA-HLA, a pan-specific prediction model that combines language model-based embeddings (ELMo) with a dual attention mechanism-self-aligned cross-attention and self-attention-to capture rich contextual features and pairwise interactions. Evaluations against 14 state-of-the-art methods on multiple benchmark datasets demonstrate that SeqDA-HLA consistently outperforms competing approaches, achieving an AUC value up to 0.9856 and accuracy as high as 0.9408. Notably, SeqDA-HLA maintains robust performance across peptide lengths (8-14) and HLA alleles, showcasing its generalizability. Beyond predictive accuracy, SeqDA-HLA offers interpretability by highlighting essential anchor residues and revealing key binding motifs, thereby aligning with experimentally validated biological insights. As a further demonstration of practical impact, we fine-tune SeqDA-HLA on an Influenza virus dataset, successfully predicting binding changes induced by single amino acid mutations. Overall, SeqDA-HLA serves as a powerful and interpretable tool for peptide-HLA binding prediction, with potential applications in epitope-based vaccine design and precision immunotherapy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".