AIMC Topic: Antigens, Neoplasm
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Immunopeptidomics-guided discovery and characterization of neoantigens for personalized cancer immunotherapy.
Science advances
May 21, 2025
Neoantigens have emerged as ideal targets for personalized cancer immunotherapy. We depict the pan-cancer peptide atlas by comprehensively collecting immunopeptidomics from 531 samples across 14 cancer and 29 normal tissues, and identify 389,165 cano...
Engineering TCR-controlled fuzzy logic into CAR T cells enhances therapeutic specificity.
Cell
Apr 11, 2025
Chimeric antigen receptor (CAR) T cell immunotherapy represents a breakthrough in the treatment of hematological malignancies, but poor specificity has limited its applicability to solid tumors. By contrast, natural T cells harboring T cell receptors...
101 Machine Learning Algorithms for Mining Esophageal Squamous Cell Carcinoma Neoantigen Prognostic Models in Single-Cell Data.
International journal of molecular sciences
Apr 4, 2025
Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in the digestive tract, characterized by a high recurrence rate and inadequate immunotherapy options. We analyzed mutation data of ESCC from public databases and...
Digital quantification of Ki67 and PRAME in challenging melanocytic lesions - A novel diagnostic tool.
Pathology, research and practice
Mar 31, 2025
The interpretation of immunohistochemical markers in melanocytic lesions possesses difficulties due to expression in non-melanocytic cells and the time-consuming, non-reproducible nature of manual assessment. A digital tool that accurately quantifies...
Unraveling the power of NAP-CNB's machine learning-enhanced tumor neoantigen prediction.
eLife
Mar 11, 2025
In this study, we present a proof-of-concept classical vaccination experiment that validates the in silico identification of tumor neoantigens (TNAs) using a machine learning-based platform called NAP-CNB. Unlike other TNA predictors, NAP-CNB leverag...
OnmiMHC: a machine learning solution for UCEC tumor vaccine development through enhanced peptide-MHC binding prediction.
Frontiers in immunology
Feb 28, 2025
The key roles of Major Histocompatibility Complex (MHC) Class I and II molecules in the immune system are well established. This study aims to develop a novel machine learning framework for predicting antigen peptide presentation by MHC Class I and I...
Computational biology and artificial intelligence in mRNA vaccine design for cancer immunotherapy.
Frontiers in cellular and infection microbiology
Jan 20, 2025
Messenger RNA (mRNA) vaccines offer an adaptable and scalable platform for cancer immunotherapy, requiring optimal design to elicit a robust and targeted immune response. Recent advancements in bioinformatics and artificial intelligence (AI) have sig...
LRMAHpan: a novel tool for multi-allelic HLA presentation prediction using Resnet-based and LSTM-based neural networks.
Frontiers in immunology
Nov 28, 2024
INTRODUCTION: The identification of peptides eluted from HLA complexes by mass spectrometry (MS) can provide critical data for deep learning models of antigen presentation prediction and promote neoantigen vaccine design. A major challenge remains in...
Personalized cancer vaccine design using AI-powered technologies.
Frontiers in immunology
Nov 8, 2024
Immunotherapy has ushered in a new era of cancer treatment, yet cancer remains a leading cause of global mortality. Among various therapeutic strategies, cancer vaccines have shown promise by activating the immune system to specifically target cancer...
Cancer Immunotherapies Ignited by a Thorough Machine Learning-Based Selection of Neoantigens.
Advanced biology
Jul 6, 2024
Identification of neoantigens, derived from somatic DNA alterations, emerges as a promising strategy for cancer immunotherapies. However, not all somatic mutations result in immunogenicity, hence, efficient tools to predict the immunogenicity of neoe...
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