Allergy & Immunology

Latest AI and machine learning research in allergy & immunology for healthcare professionals.

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Showing 2101-2120 of 6,939 articles

LoFT-TCR: A LoRA-based Fine-tuning Framework for TCR-Antigen Binding Prediction

T cells recognize and eliminate diseased cells by binding their T cell receptors (TCRs) to short endogenous peptides (antigens) presented on the cell surface. Such interactions are central to adaptive immunity, yet current experimental approaches to identify TCR-antigen binding pairs remain labor-intensive and constrained by limited reagents. Here, we propose LoFT-TCR, a low-rank adaptation (LoRA)...

Citizen science gamers enable automated flow cytometry gating through machine learning

Manual flow cytometry gating requires up to one hour per sample with 32% inter-expert variability, creating critical bottlenecks in immunological research reproducibility. To address this, we developed flowMagic, a machine learning algorithm for automated gating that is trained on both expert-curated data (template data) and crowdsourced annotations from citizen science gaming. Through EVE Online,...

Application and Characterization of the Multiple Instance Learning Framework in Flow Cytometry

For decades, flow cytometry has allowed for single-cell profiling based on selected biomarkers and is widely used in both clinical and research settin...

Adaptive transcriptional strategies underpin the host-specific virulence of the generalist oomycete Phytophthora capsici during early crown infection

Phytophthora capsici is a destructive, broad-host-range oomycete responsible for substantial losses in global agriculture. While most transcriptomic s...

Integrated analysis implicates novel insights of NMB into lactate metabolism and immune response prediction in primary glioblastoma

Glioblastoma (GBM), the most aggressive primary brain tumor in adults, exhibits profound treatment resistance and poor prognosis. Despite advances in ...

An Immuno-Linguistic Transformer for Multi-Scale Modeling of T-Cell Spatiotemporal Dynamics

Understanding the spatiotemporal dynamics of T-cell clones is a critical challenge in immunology and immunotherapy, with direct implications for cance...

Base-editing a single missense mutation in A20 enhances CAR-T cell efficacy

T cell exhaustion limits the efficacy of cancer immunotherapies. Here, we performed genome-wide loss-of-function screening in repetitively stimulated ...

mosna reveals different types of cellular interactions predictive of response to immunotherapies and survival in cancer

Spatially resolved omics enable the discovery of tissue organization of biological or clinical importance. Despite the existence of several methods, p...

AlphaMissense pathogenicity scores predict response to immunotherapy and enhances the predictive capability of tumor mutation burden

Tumor Mutational Burden (TMB) is a widely used biomarker for selecting cancer patients for immune checkpoint inhibitor (ICI) therapy. However, TMB alo...

Multivariate analysis of glycogenes reveals coordinated regulation of immunoglobulin glycosylation in an immortalized human B cell system

While neutralizing ability has traditionally been considered the most important antibody function, appreciation has grown for Fc-mediated ‘extra-neutr...

RoBep: A Region-Oriented Deep Learning Model for B-Cell Epitope Prediction

Accurate in silico identification of B-cell epitope residues is crucial for antibody design and structure-guided vaccine development. Although recent ...

Comparative Analysis of Pathology Foundation Models for Automated Detection of Tertiary Lymphoid Structures in H&E-Stained Digital Pathology Images

Tertiary lymphoid structures (TLS) have been observed in solid tumors and have been associated with better outcomes in patients treated with immunothe...

Characterizing spatial functional microniches with SpaceTravLR

The advent of spatial omics has revolutionized our understanding of tissue biology; however, these technologies remain largely descriptive and do not ...

Predictive power of different Akkermansia phylogroups in clinical response to PD-1 blockade against non-small cell lung cancer

Immune checkpoint blockade has emerged as a promising form of cancer therapy. However, only some patients respond to checkpoint inhibitors, while a si...

H3BERTa: A CDR-H3 specific language model for antibody repertoire analysis

Antibodies are central to immune defense and therapeutic design, yet predicting which sequences confer functional activity remains challenging. Deep l...

IMMUNIA: A Multi-LLM Reasoning Agent for Immunoregulatory Surfaceome Discovery

Biomarker discovery for immunotherapy often requires reasoning across complex immune contexts. We present IMMUNIA, a multi-large-language-model (multi...

From Tasks to Topology: Dorsal and Ventral Streams Emerge in Optimized Neural Networks

The primate visual system is organized into dorsal and ventral pathways, classically linked to visuomotor control and perception. A long-standing ques...

Targeting peptide–MHC complexes with designed T cell receptors and antibodies

Class I major histocompatibility complexes (MHCs), expressed on the surface of all nucleated cells, present peptides derived from intracellular protei...

Self-supervised AI reveals a lethal discohesive phenotype in lung adenocarcinoma

Applications of artificial intelligence (AI) to histopathology are now common, but most require supervision which inherently limits their scope. By us...

TEIP: A Compact, Open-Source Framework for Predicting Tumor Epitope Immunogenicity in Glioblastoma Using Deep Learning and Multi-Modal Biological Features

This work introduces a modular, open-source computational pipeline for glioblastoma (GBM) vaccine design that integrates omics-based OIP5 target disco...

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