Title: ​ImmunoGraph: Accelerated and Equitable Representation Learning for Large-Scale Immune Networks​
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
Keywords: Representation Learning, Immunoinformatics, Fairness, Hardware Acceleration, Visual Analytics, Graph Representation Learning, Metric Learning, Biological Networks

Abstract: Large-scale analysis of adaptive immune repertoires is impeded by the quadratic cost of pairwise affinity computations and by dataset imbalances that mask clinically relevant subpopulations. We introduce ImmunoGraph, a practical pipeline that addresses both challenges by combining antigen-aware, near-subquadratic retrieval with GPU-accelerated similarity kernels, learned multimodal fusion of alignment and embedding signals, and fairness-constrained clustering. ImmunoGraph uses compact MinHash prefiltering to sharply reduce candidate sets, a differentiable gating module to weight complementary affinity channels per pair, and an automated calibration routine that enforces proportional subgroup representation. Evaluated on large viral and cancer repertoires, the system yields large gains in throughput and memory use while retaining or improving recall@k, cluster purity, and equity across subgroups. ImmunoGraph therefore provides a scalable, bias-aware platform for repertoire mining and downstream translational applications such as vaccine target discovery.

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