TransNRank: Towards Accurate Neoantigen Ranking with Transformer
Personalized neoantigen prediction is challenging due to the scarcity of positive samples, the noise of the experimental data, the severe class imbalance trait and the complex of immunogenicity features. Prior arts, such as linear regression and XGBoost fail to model long-range dependencies and contextual relationships within peptide features, therefore the performance of neoantigen positive recall rate is limited. In this paper, we present a novel deep learning framework based on Transformer, coined as TransNRank. By leveraging the self-attention mechanism, our model captures both local and global feature contexts, enabling more accurate recognition of immunogenic neoantigens. A positive...
| Authors | Zhiyin An, Yuenan Hou, Shumeng Duan et al. |
|---|---|
| Primary category | cs.CE |
| Published | 3 August 2026 |
| Last updated at source | 8 August 2026 |
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