TransNRank: Towards Accurate Neoantigen Ranking with Transformer
Quick summary
arXiv:2608.01924v2 Announce Type: replace-cross Abstract: 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 sel
Key takeaways
- arXiv:2608.01924v2 Announce Type: replace-cross Abstract: 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.
Why it matters
“TransNRank: Towards Accurate Neoantigen Ranking with Transformer” highlights the need for repeatable measurement rather than a single impressive demonstration. Independent validation across datasets and clearly stated limitations determine whether a result can guide product decisions.
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