​ImmunoGraph: Accelerated and Equitable Representation Learning for Large-Scale Immune Networks​

Rong Fu, Zijian Zhang, Dayang Wang, Simon James Fong, Anqi Zhao, weizhi Tang

Keywords: Representation Learning, Immunoinformatics, Fairness, Hardware Acceleration, Visual Analytics, Graph Representation Learning, Metric Learning, Biological Networks

TL;DR: ImmunoGraph accelerates immune network exploration through fair representation learning, interactive visualization, and scalable hardware-aware computation for equitable biological insights.

Abstract: 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 ImmunoGraph, 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 ImmunoGraph 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, ImmunoGraph offers a scalable, bias-aware platform for repertoire mining and downstream translational tasks such as vaccine target prioritization and biomarker discovery.

Primary Area: applications to physical sciences (physics, chemistry, biology, etc.)

Submission Number: 4623