Allergy & Immunology

VaxOptiML: leveraging machine learning for accurate prediction of MHC-I and II epitopes for optimized cancer immunotherapy.

Cancer immunotherapy hinges on accurate epitope prediction for advancing vaccine development. VaxOptiML (available at https://vaxoptiml.streamlit.app/ ) is an integrated pipeline designed to enhance epitope prediction and prioritization. This study aims to develop and deploy a robust tool for accurate prediction and prioritization of highly immunogenic and optimized MHC-I and MHC-II T-cell epitope...

Milk ladder: Who? When? How? Where? with the lowest risk of reaction.

The milk ladder (ML) approach, which is the gradual reintroduction of the milk allergen from the least allergenic forms to the most allergenic forms into the diet of the patients, has been utilized mostly in non-IgE-mediated but in some countries also in IgE-mediated-CMPA due to its possible benefits which include nutrition, quality of life and tolerance induction. Despite increasing interest, so ...

Integrated explainable machine learning and multi-omics analysis for survival prediction in cancer with immunotherapy response.

To demonstrate the efficacy of machine learning models in predicting mortality in melanoma cancer, we developed an interpretability model for better u...

Harnessing machine learning and multi-omics to explore tumor evolutionary characteristics and the role of AMOTL1 in prostate cancer.

Although recent advancements have shed light on the crucial role of coordinated evolution among cell subpopulations in influencing disease progression...

Machine learning based on multiplatform tests assists in subtype classification of mature B-cell neoplasms.

Mature B-cell neoplasms (MBNs) are clonal proliferative diseases encompassing over 40 subtypes. The WHO classification (morphology, immunology, cytoge...

Identification of diagnostic biomarkers and molecular subtype analysis associated with m6A in Tuberculosis immunopathology using machine learning.

Tuberculosis (TB), ranking just below COVID-19 in global mortality, is a highly complex infectious disease involving intricate immunological molecules...

Accurate Airway Tree Segmentation in CT Scans via Anatomy-Aware Multi-Class Segmentation and Topology-Guided Iterative Learning.

Intrathoracic airway segmentation in computed tomography is a prerequisite for various respiratory disease analyses such as chronic obstructive pulmon...

Integrated multi-omics and machine learning reveal a gefitinib resistance signature for prognosis and treatment response in lung adenocarcinoma.

Gefitinib resistance (GR) presents a significant challenge in treating lung adenocarcinoma (LUAD), highlighting the need for alternative therapies. Th...

Multiparametric MRI-based Machine Learning Radiomics for Predicting Treatment Response to Transarterial Chemoembolization Combined with Targeted and Immunotherapy in Unresectable Hepatocellular Carcinoma: A Multicenter Study.

RATIONALE AND OBJECTIVES: To develop and validate multiple machine learning predictive models incorporating clinical features and pretreatment multipa...

The role of artificial intelligence in immune checkpoint inhibitor research: A bibliometric analysis.

Immune checkpoint inhibitors (ICIs) are revolutionizing cancer treatment, and Artificial Intelligence (AI) is a key player in this field. A comprehens...

A comprehensive neuroimaging review of the primary and metastatic brain tumors treated with immunotherapy: current status, and the application of advanced imaging approaches and artificial intelligence.

Cancer immunotherapy has emerged as a novel clinical therapeutic option for a variety of solid tumors over the past decades. The application of immuno...

Targeted isolation and AI-based analysis of edible fungal polysaccharides: Emphasizing tumor immunological mechanisms and future prospects as mycomedicines.

Edible fungal polysaccharides have emerged as significant bioactive compounds with diverse therapeutic potentials, including notable anti-tumor effect...

Machine Learning Integration with Single-Cell Transcriptome Sequencing Datasets Reveals the Impact of Tumor-Associated Neutrophils on the Immune Microenvironment and Immunotherapy Outcomes in Gastric Cancer.

The characteristics of neutrophils play a crucial role in defining the tumor inflammatory environment. However, the function of tumor-associated neutr...

Potential diagnostic biomarkers in heart failure: Suppressed immune-associated genes identified by bioinformatic analysis and machine learning.

Heart failure (HF) threatens tens of millions of people's health worldwide, which is the terminal stage in the development of majority cardiovascular ...

Predicting Asthma Exacerbations Using Machine Learning Models.

INTRODUCTION: Although clinical, functional, and biomarker data predict asthma exacerbations, newer approaches providing high accuracy of prognosis ar...

Machine learning identifies immune-based biomarkers that predict efficacy of anti-angiogenesis-based therapies in advanced lung cancer.

BACKGROUND: The anti-angiogenic drugs showed remarkable efficacy in the treatment of lung cancer. Nonetheless, the potential roles of the intra-tumora...

Predicting paediatric asthma exacerbations with machine learning: a systematic review with meta-analysis.

BACKGROUND: Asthma exacerbations in children pose a significant burden on healthcare systems and families. While traditional risk assessment tools exi...

Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis.

Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship betwe...

Integration of Machine Learning and Experimental Validation to Identify Anoikis-Related Prognostic Signature for Predicting the Breast Cancer Tumor Microenvironment and Treatment Response.

Anoikis-related genes (ANRGs) are crucial in the invasion and metastasis of breast cancer (BC). The underlying role of ANRGs in the prognosis of brea...

Predicting and Monitoring Immune Checkpoint Inhibitor Therapy Using Artificial Intelligence in Pancreatic Cancer.

Pancreatic cancer remains one of the most lethal cancers, primarily due to its late diagnosis and limited treatment options. This review examines the ...