TrambaHLApan: a transformer and mamba-based neoantigen prediction method considering both antigen presentation and immunogenicity

Neoantigens represent ideal targets for tumor immunotherapy. This study introduces TrambaHLApan, a neoantigen prediction method that utilizes Transformer and Mamba models to consider both antigen presentation potential (TrambaHLApan-EL) and immunogenicity (TrambaHLApan-IM). Data S1: Antigen presentation training set Data S2: Immunogenicity training set Data S3: Allele24 independent antigen presentation test dataset Data S4: Allele36 independent antigen presentation test set Data S5: Cancer neoantigen immunogenicity test set (GBM) Data S6: Cancer neoantigen immunogenicity test set (MANAFEST) fivefold_val_flags(DataS1): Five-fold cross-validation division file for DataS1 fivefold_val_flags(DataS2): Five-fold cross-validation division file for DataS2新抗原（neoantigen）是肿瘤免疫治疗的理想靶点。本研究提出TrambaHLApan——一种融合Transformer与Mamba模型的新抗原预测方法，可同时考量抗原呈递潜能（TrambaHLApan-EL）与免疫原性（TrambaHLApan-IM）。 数据集S1：抗原呈递训练集 数据集S2：免疫原性训练集 数据集S3：Allele24独立抗原呈递测试数据集 数据集S4：Allele36独立抗原呈递测试集 数据集S5：肿瘤新抗原免疫原性测试集（GBM） 数据集S6：肿瘤新抗原免疫原性测试集（MANAFEST） fivefold_val_flags(DataS1)：数据集S1的五折交叉验证划分文件 fivefold_val_flags(DataS2)：数据集S2的五折交叉验证划分文件