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.
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.
arXiv:2606. 29949v1 Announce Type: cross Abstract: H&E-stained whole-slide images offer cohort-scale availability and rich spatial context but lack molecular specificity, whereas bulk RNA-seq provides transcriptome-wide resolution at high cost with limited archival availability.
arXiv:2606. 04154v1 Announce Type: cross Abstract: Antibodies neutralize foreign antigens by binding to specific surface regions called epitopes.
arXiv:2606. 28659v1 Announce Type: cross Abstract: High-fidelity molecular docking simulations can produce biologically relevant estimates of epitope-receptor binding affinity but are computationally expensive and therefore limit the number of candidates that can be screened for vaccine design.
arXiv:2606. 05139v1 Announce Type: new Abstract: The rapid advancement of high-throughput sequencing has led to large, high-dimensional omics datasets.
arXiv:2603. 13431v3 Announce Type: replace-cross Abstract: Computational antibody design has seen rapid methodological progress, with dozens of deep generative methods proposed in the past three years, yet the field lacks a standardized benchmark for fair comparison and model development.
arXiv:2607. 22314v1 Announce Type: new Abstract: Multiple Sequence Alignments (MSAs) provide protein language models with explicit evolutionary context, but their large depth makes subsampling unavoidable under limited token budgets.
DuaDeep-SeqAffinity is a sequence-only deep learning framework that predicts antibody–antigen binding affinity directly from primary amino acid sequences, eliminating the need for resolved 3D structures. The model processes the antigen and the antibody heavy and light chains as three independent streams, each embedded with a frozen ESM‑2 protein language model and passed through parallel Transformer and CNN branches before late fusion. On a sequence‑disjoint split of the AbRank benchmark, it achieves a Pearson correlation of 0.683, an R² of 0.460, and a pairwise ranking AUC of 0.895, outperforming single‑branch ablations and showing attention to CDR loops and epitope residues.
arXiv:2607. 05846v1 Announce Type: cross Abstract: Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery.
Accurate ranking of antibody candidates according to their binding affinity is essential for therapeutic antibody discovery. However, existing methods treat affinity comparisons independently and ignore the contextual information encoded in other labeled comparisons, limiting their ability to capture antigen-specific binding landscapes.
The paper introduces Conserved Immune Topology (CIT), a lightweight spatial representation that enhances cross‑cancer MSI‑H prediction by augmenting pathology foundation‑model embeddings with immune‑related descriptors. CIT identifies immune‑associated tiles via unsupervised clustering and encodes features such as tertiary lymphoid structures, peritumoral immune reactions, tumor‑infiltrating lymphocyte density, and immune‑tumor mixing, all without requiring annotations or target‑domain data. In cross‑site and cross‑cancer experiments on CPTAC‑COAD and TCGA‑STAD cohorts, CIT improved zero‑shot TransMIL AUC from 0.6627 to 0.7161, demonstrating that spatial immune topology can provide an organ‑invariant representation for MSI‑H prediction.
arXiv:2605. 21610v2 Announce Type: replace Abstract: Antibody design methods condition on antigen structure to generate complementarity-determining regions (CDR), yet a systematic evaluation of baseline methods reveals that they largely ignore the antigen input.