你要是做免疫组库时间序列分析，这个模型把神经ODE和Transformer揉在一起，能处理克隆动态和复发，比静态模型强。
DynImmune-BERT是一种连续时间免疫组库模型，用于患者免疫状态预测。该模型结合了深度自适应中心对数比初始化、克隆存在门控神经ODE动态和边界邻域自注意力。它通过低秩元适配器初始化复发克隆型，参数数量独立于观察到的克隆数。在纵向T细胞受体库数据集上的评估显示，事件感知的时间建模能增强静态编码器性能，但小规模外部队列需谨慎解读。
DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers
Longitudinal T cell receptor repertoires contain signals of clonal expansion, contraction, disappearance, and reappearance after immune perturbation. Static repertoire language models usually summarize a sample as a bag of sequences, so the sampling interval, sequencing depth, and clone presence pattern are only weakly represented. This paper presents DynImmune-BERT, a continuous time repertoire model for patient level immune status prediction. The method combines depth adaptive centered log ratio initialization, clone presence gated Neural ordinary differential equation dynamics, bounded neighborhood self attention, event based state restart, and a hybrid transport objective that supervises dominant and rare clone mass. A low rank meta adapter initializes reappearing clonotypes while keeping the parameter count independent of the number of observed clones. The evaluation separates literature reported baselines from internally controlled temporal comparisons, reports uncertainty for small external cohorts, adds calibration and threshold diagnostics, and visualizes latent clone trajectories and attention neighborhoods. The results indicate that event aware temporal modeling can complement strong static encoders when longitudinal repertoire structure is available, while small external cohorts and protocol differences require cautious interpretation.