TransNRank: Towards Accurate Neoantigen Ranking with Transformer
2026-08-03 • Computational Engineering, Finance, and Science
Computational Engineering, Finance, and ScienceArtificial Intelligence
AI summaryⓘ
The authors developed a new deep learning method called TransNRank to better predict neoantigens, which are important for cancer treatment but hard to identify. Their model uses a Transformer-based approach to understand complex patterns in peptide data, improving how well it finds true neoantigens compared to older methods. They also addressed the problem of having few positive examples by weighting these more during training. Tests showed their method is faster and more accurate, and they found some unexpected biological features are important in the predictions. This work improves neoantigen prediction without needing overly complicated inputs.
neoantigenTransformerself-attentionclass imbalanceimmunogenicitypeptide featuresdeep learningpositive-aware trainingmutation anchorTCGA expression level
Authors
Zhiyin An, Yuenan Hou, Shumeng Duan, Yiming Zhou, Yuanting Zheng, Leming Shi
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 self-attention mechanism, our model captures both local and global feature contexts, enabling more accurate recognition of immunogenic neoantigens. A positive-aware training objective is utilized to handle the class imbalance problem, assigning more weights to those few positive samples. Extensive experiments are performed on NCI, TESLA and HiTIDE datasets. Notably, our TransNRank can push the upper bound top 20 recall rate of neoantigen prediction from 46.9% (45 from 96) to 53.1% (51 from 96), while reducing the training epochs from 200 epochs to 20 epochs. Furthermore, we analyze the features contribution based on TransNRank and find that the mutation at anchor and TCGA expression level play an unexpected important role in neoantigen prediction, and removing insignificant features to reduce the input dimensionality of peptides does not drastically impair the overall performance of the model. Our paradigm not only streamlines the prediction pipeline but also sets a new state-of-the-art for neoantigen discovery, with broad implications for accurate immuno-oncology.