Allergy

Artificial intelligence in lung cancer: current applications, future perspectives, and challenges.

Artificial intelligence (AI) has significantly impacted various fields, including oncology. This comprehensive review examines the current applications and future prospects of AI in lung cancer research and treatment. We critically analyze the latest AI technologies and their applications across multiple domains, including genomics, transcriptomics, proteomics, metabolomics, immunomics, microbiomi...

Artificial intelligence in pediatric allergy research.

UNLABELLED: Atopic dermatitis, food allergy, allergic rhinitis, and asthma are among the most common diseases in childhood. They are heterogeneous diseases, can co-exist in their development, and manifest complex associations with other disorders and environmental and hereditary factors. Elucidating these intricacies by identifying clinically distinguishable groups and actionable risk factors will...

Detection of Patient-Level Immunotherapy-Related Adverse Events (irAEs) from Clinical Narratives of Electronic Health Records: A High-Sensitivity Artificial Intelligence Model.

PURPOSE: We developed an artificial intelligence (AI) model to detect immunotherapy -related adverse events (irAEs) from clinical narratives of electr...

Leveraging artificial intelligence and machine learning to accelerate discovery of disease-modifying therapies in type 1 diabetes.

Progress in developing therapies for the maintenance of endogenous insulin secretion in, or the prevention of, type 1 diabetes has been hindered by li...

Geometric deep learning improves generalizability of MHC-bound peptide predictions.

The interaction between peptides and major histocompatibility complex (MHC) molecules is pivotal in autoimmunity, pathogen recognition and tumor immun...

Optimizing the management of immune-related adverse events and survival in patients with thoracic cancer receiving immunotherapy through artificial intelligence (electronic patient-reported outcomes): The IMPATHI study.

INTRODUCTION: The lung cancer continues to be the primary cause of cancer-related deaths, despite significant advancements in treatment through the in...

The establishment of PD-1 inhibitor treatment prognosis model based on dynamic changes of peripheral blood indexes in patients with advanced lung squamous cell carcinoma.

BACKGROUND: Unlike patients with lung adenocarcinoma, patients with lung squamous cell carcinoma (LUSC) do not derive significant benefits from target...

Potential shared mechanisms in atopic dermatitis and type 2 diabetes identified via transcriptomic and machine learning approaches.

Although atopic dermatitis (AD) and type 2 diabetes mellitus (T2DM) may appear clinically and pathophysiologically unrelated, AD is a common skin dise...

Functional MRGPRX2 expression on peripheral blood-derived human mast cells increases at low seeding density and is suppressed by interleukin-9 and fetal bovine serum.

Primary human mast cells (MC) obtained through culturing of blood-derived MC progenitors are the preferred model for the study of MRGPRX2- IgE-media...

Multi-omics analysis and experiments uncover the function of cancer stemness in ovarian cancer and establish a machine learning-based model for predicting immunotherapy responses.

BACKGROUND: The heterogeneity of cancer makes it challenging to predict its response to immunotherapy, highlighting the need to find reliable biomarke...

Integrating machine learning, bioinformatics and experimental verification to identify a novel prognostic marker associated with tumor immune microenvironment in head and neck squamous carcinoma.

Head and neck squamous carcinoma (HNSC), characterized by a high degree of malignancy, develops in close association with the tumor immune microenviro...

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.

INTRODUCTION: Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulat...

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/...

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 i...

Accelerating virtual patient generation with a Bayesian optimization and machine learning surrogate model.

The pharmaceutical industry has increasingly adopted model-informed drug discovery and development (MID3) to enhance productivity in drug discovery an...

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...

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...

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...