用少量样本快速生成免疫特征，让模型在数据少时也能准确分析免疫反应。
SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes
- 通过轻量任务描述词和原型字典动态生成小规模适配模块。
- 仅需少量样本即可在新任务上快速部署，且不需全模型微调。
- 保留可解释性，能关联预测结果与具体序列信号，适合临床研究。
T细胞受体谱型分析为疾病检测和免疫监测提供了生物合理的信号，但实际应用受限于标签稀疏、队列异质性以及大模型编码器适应新任务的计算开销。我们提出一种框架，从基于谱型探针和嵌入统计量的轻量级任务描述符中学习原型字典，动态合成紧凑的任务特定参数化。该合成生成的小型适配模块可应用于冻结的预训练主干网络，实现仅需少量支持样本即可立即适应新任务，无需完整模型微调。架构通过基序感知探针和校准的基序发现流程保持可解释性，使预测决策与序列级信号相关联。这些组件共同构建了一条高效、低样本依赖且可解释的路径，将谱型驱动的模型应用于标签数据稀缺、计算资源受限的多样化临床与科研场景。
原文摘要 · Abstract (English)
Repertoire-level analysis of T cell receptors offers a biologically grounded signal for disease detection and immune monitoring, yet practical deployment is impeded by label sparsity, cohort heterogeneity, and the computational burden of adapting large encoders to new tasks. We introduce a framework that synthesizes compact task-specific parameterizations from a learned dictionary of prototypes conditioned on lightweight task descriptors derived from repertoire probes and pooled embedding statistics. This synthesis produces small adapter modules applied to a frozen pretrained backbone, enabling immediate adaptation to novel tasks with only a handful of support examples and without full model fine-tuning. The architecture preserves interpretability through motif-aware probes and a calibrated motif discovery pipeline that links predictive decisions to sequence-level signals. Together, these components yield a practical, sample-efficient, and interpretable pathway for translating repertoire-informed models into diverse clinical and research settings where labeled data are scarce and computational resources are constrained.
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