arXiv Machine Learning July 23, 2026 By Rong Fu, Muge Qi, Yang Li, Yabin Jin, Jiekai Wu, Chunlei Meng, Juntao Gao, Li Bao, Qi Zhao, Wei Luo, Youjin Wang, Simon Fong

SwiftRepertoire: Few-Shot Immune-Signature Synthesis via Dynamic Kernel Codes

Read the original on arXiv Machine Learning →

arXiv:2602. 01051v5 Announce Type: replace Abstract: 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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv AI

Jul 23

SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

arXiv:2602. 17330v5 Announce Type: replace-cross 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.

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rag efficiency multimodal safety

arXiv Machine Learning

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Structure-Regularized Interpretable TCR-Epitope Prediction

arXiv:2606. 30902v1 Announce Type: cross Abstract: T cell receptor (TCR)-epitope binding prediction is essential for understanding adaptive immunity and developing immunotherapies.

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llms rag benchmarks safety

arXiv Machine Learning

Jun 4

EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning

arXiv:2606. 04154v1 Announce Type: cross Abstract: Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes.

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efficiency benchmarks

arXiv AI

Jun 4

You Only Train Once: Differentiable Subset Selection for Omics Data

arXiv:2512. 17678v2 Announce Type: replace-cross Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling.

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efficiency benchmarks safety

arXiv Machine Learning

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DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers

arXiv:2607. 17244v1 Announce Type: new Abstract: Longitudinal T cell receptor repertoires contain signals of clonal expansion, contraction, disappearance, and reappearance after immune perturbation.

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arXiv Machine Learning

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Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis

arXiv:2606. 09558v1 Announce Type: cross Abstract: Motivation: Transformer-based models are increasingly applied to large-scale single-cell transcriptomics, showing strong performance through self-supervised learning on millions of cells.

By Mikele Milia, Louis Fabrice Tshimanga, Henning Mueller, Manfredo Atzori, Barbara Di Camillo

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Related stories

SubQuad: Near-Quadratic-Free Structure Inference with Distribution-Balanced Objectives in Adaptive Receptor framework

Structure-Regularized Interpretable TCR-Epitope Prediction

EpiFormer: Learning Antigen-Antibody Interactions for Epitope Prediction via Geometric Deep Learning

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DynImmune-BERT: Dynamic Immune Repertoire Modeling with Neural ODE Driven Continuous Transformers

Integrating gene regulatory priors into Transformer attention with scTransformer for interpretable scRNA-seq analysis