Allergy

DeepPROTECTNeo: A Deep learning-based Personalized and RV-guided Optimization tool leveraging TCR Epitope interaction using Context-aware Transformers

The development of personalized cancer vaccines relies heavily on accurately identifying neoepitopes capable of eliciting strong immune responses. T cell receptor (TCR)-epitope interactions are fundamental to cancer immunotherapy. Despite significant advances, computational approaches primarily focus on epitope-major histocompatibility complex (MHC) binding, often overlooking the critical contribu...

CART-GPT: A T Cell-Informed AI Linguistic Framework for Interpreting Neurotoxicity and Therapeutic Outcomes in CAR-T Therapy

Chimeric antigen receptor (CAR) T cell therapy holds transformative potential for hematologic malignancies, yet predicting patient-specific treatment efficacy and neurotoxicity remains a major clinical challenge due to the complex and heterogeneous nature of the infused CAR-T cell populations. Here, we introduce CART-GPT, a transformer-based model fine-tuned on a curated atlas of 1.12 million CAR-...

Reengineering the antigen optimization process for superior neoantigen vaccine design

Identifying effective neoantigen sequences is essential for enhancing anti-tumor immunity. However, the vast sequence space (>109 possible peptides) a...

The hidden predictors of human haematopoietic clonal fate

Human haematopoietic stem and progenitor cells (HSPCs) exhibit heterogeneous lineage output, but the molecular programs underlying clonal fate remain ...

Pocket-based molecule generation with an SE(3)-equivariant language model leads to a potent and selective HPK1 inhibitor with in vivo efficacy

Deep learning shows promise in structure-based drug discovery, yet challenges persist in generating pharmacologically plausible molecules with valid 3...

Translating clinical gene sequencing into a foundational representation of tumor subtype

While gene sequencing is routine in cancer care, translating sequences into treatment decisions remains a challenge. Here we introduce MutationProject...

SpaPheno: Linking Spatial Transcriptomics to Clinical Phenotypes with Interpretable Machine Learning

Linking spatial transcriptomic data to clinically relevant phenotypes is essential for advancing spatially informed precision oncology. Here, we prese...

BLMPred: predicting linear B-cell epitopes using pre-trained protein language models and machine learning

B-cells get activated through interaction with B-cell epitopes, a specific portion of the antigen. Identification of B-cell epitopes is crucial for a ...

Fourier transform infrared spectroscopy enables rapid species discrimination across Malassezia and strain-level typing in M. pachydermatis

Malassezia pachydermatis is a zoophilic yeast found on the skin and in the outer ear canal of many mammals. It normally maintains a commensal lifestyl...

Accurate and scalable multi-disease classification from adaptive immune repertoires

Machine learning models trained on paratope-similarity networks have shown superior accuracy compared with clonotype-based models in binary disease cl...

Iterative improvement of deep learning models using synthetic regulatory genomics

Deep learning models can accurately reconstruct genome-wide epigenetic tracks from the reference genome sequence alone. But it is unclear what predict...

Tricked by Edge Cases: Can Current Approaches Lead to Accurate Prediction of T-Cell Specificity with Machine Learning?

The ability to predict T cell receptor (TCR) specificity from sequence could transform immunotherapy, vaccine development, and our understanding of im...

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 ...

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...

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...