DynImmune-BERT：基于神经常微分方程驱动的连续变换器的动态免疫组库建模
DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers
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中文总结 AI 辅助
研究针对纵向T细胞受体组库建模问题，提出DynImmune-BERT模型，结合多种方法，能在有纵向组库结构时补充静态编码器，通过特定评估方式得出结果，对小外部队列和协议差异需谨慎解读。
中文摘要 AI 辅助
纵向T细胞受体组库包含免疫扰动后克隆扩增、收缩、消失和重新出现的信号。静态组库语言模型通常将样本总结为一袋序列，因此采样间隔、测序深度和克隆存在模式仅得到微弱体现。本文提出了DynImmune-BERT，一种用于患者水平免疫状态预测的连续时间组库模型。该方法结合了深度自适应中心对数比初始化、克隆存在门控神经常微分方程动力学、有界邻域自注意力、基于事件的状态重启以及监督优势和稀有克隆质量的混合传输目标。一个低秩元适配器初始化重新出现的克隆型，同时保持参数数量与观察到的克隆数量无关。评估将文献报道的基线与内部控制的时间比较分开，报告小外部队列的不确定性，添加校准和阈值诊断，并可视化潜在克隆轨迹和注意力邻域。结果表明，当纵向组库结构可用时，事件感知时间建模可以补充强大的静态编码器，而小外部队列和协议差异需要谨慎解释。
英文摘要
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.
发表机构
- University of Macau（澳门大学）
- Hanyang University（汉阳大学）
- Zhejiang University（浙江大学）
- Wuhan University（武汉大学）
- Tsinghua University（清华大学）
- South China University of Technology（华南理工大学）
- University of Liverpool（利物浦大学）
机构由 AI 辅助整理，请以论文原文为准。
