Comparative analysis of adaptive immune repertoires at population scale is hampered by two practical bottlenecks: the near-quadratic cost of pairwise affinity evaluations and dataset imbalances that obscure clinically important minority clonotypes. We introduce SubQuad, an end-to-end pipeline that addresses these challenges by combining antigen-aware, near-subquadratic retrieval with GPU-accelerated affinity kernels, learned multimodal fusion, and fairness-constrained clustering. The system employs compact MinHash prefiltering to sharply reduce candidate comparisons, a differentiable gating module that adaptively weights complementary alignment and embedding channels on a per-pair basis, and an automated calibration routine that enforces proportional representation of rare antigen-specific subgroups. On large viral and tumor repertoires SubQuad achieves measured gains in throughput and peak memory usage while preserving or improving recall@k, cluster purity, and subgroup equity. By co-designing indexing, similarity fusion, and equity-aware objectives, SubQuad offers a scalable, bias-aware platform for repertoire mining and downstream translational tasks such as vaccine target prioritization and biomarker discovery.

翻译：群体规模下的适应性免疫库比较分析面临两个实际瓶颈：近乎二次方的成对亲和力评估成本，以及掩盖临床重要稀有克隆型的数据集失衡。我们提出SubQuad这一端到端流水线，通过结合抗原感知的近二次方亚检索、GPU加速亲和力核函数、学习型多模态融合及公平约束聚类来解决这些挑战。该系统采用紧凑型MinHash预过滤以大幅减少候选比较，设计可微分门控模块在逐对基础上自适应加权互补比对与嵌入通道，并通过自动化校准程序确保稀有抗原特异性亚群的均衡表征。在大型病毒与肿瘤免疫库实验中，SubQuad在保持或改善召回率@k、聚类纯度及亚群公平性的同时，实现了吞吐量与峰值内存使用的可量化提升。通过协同设计索引构建、相似性融合与公平性感知目标，SubQuad为免疫库挖掘及下游转化任务（如疫苗靶点优先级排序与生物标志物发现）提供了可扩展且具备偏差感知能力的平台。