Allergy & Immunology

Future of Radiotherapy in Nasopharyngeal Carcinoma.

Nasopharyngeal carcinoma (NPC) is a malignancy with unique clinical biological profiles such as asso...

Predicting asthma attacks in primary care: protocol for developing a machine learning-based prediction model.

INTRODUCTION: Asthma is a long-term condition with rapid onset worsening of symptoms ('attacks') whi...

Cellular frustration algorithms for anomaly detection applications.

Cellular frustrated models have been developed to describe how the adaptive immune system works. The...

Applying machine learning to forecast daily Ambrosia pollen using environmental and NEXRAD parameters.

Approximately 50 million Americans have allergic diseases. Airborne plant pollen is a significant tr...

Using machine learning to examine the relationship between asthma and absenteeism.

In this study, we found that machine learning was able to effectively estimate student learning outc...

Automatic Multi-Level In-Exhale Segmentation and Enhanced Generalized S-Transform for wheezing detection.

BACKGROUND AND OBJECTIVE: Wheezing is a common symptom of patients caused by asthma and chronic obst...

Estimating the daily pollen concentration in the atmosphere using machine learning and NEXRAD weather radar data.

Millions of people have an allergic reaction to pollen. The impact of pollen allergies is on the ris...

IAPSO-AIRS: A novel improved machine learning-based system for wart disease treatment.

Wart disease (WD) is a skin illness on the human body which is caused by the human papillomavirus (H...

Applying Deep Neural Networks and Ensemble Machine Learning Methods to Forecast Airborne Pollen.

Allergies to airborne pollen are a significant issue affecting millions of Americans. Consequently, ...

Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer.

Microsatellite instability determines whether patients with gastrointestinal cancer respond exceptio...

Artificial intelligence and immunotherapy.

Seroprevalence of antibodies for pertussis and diphtheria among people leaving or entering China: a cross-sectional study.

INTRODUCTION: Despite high population immunity, pertussis remains one of the leading causes of vacci...

Deep learning facilitates the diagnosis of adult asthma.

BACKGROUND: We explored whether the use of deep learning to model combinations of symptom-physical s...

Data augmentation in dermatology image recognition using machine learning.

BACKGROUND: Each year in the United States, over 80 million people are affected by acne, atopic derm...

Capturing the differences between humoral immunity in the normal and tumor environments from repertoire-seq of B-cell receptors using supervised machine learning.

BACKGROUND: The recent success of immunotherapy in treating tumors has attracted increasing interest...

Level of neo-epitope predecessor and mutation type determine T cell activation of MHC binding peptides.

BACKGROUND: Targeting epitopes derived from neo-antigens (or "neo-epitopes") represents a promising ...

Do no harm: Natural language processing of social media supports safety of aseptic allergen immunotherapy procedures.

An ensemble learning method for asthma control level detection with leveraging medical knowledge-based classifier and supervised learning.

Approximately 300 million people are afflicted with asthma around the world, with the estimated deat...

Reporting and connecting cell type names and gating definitions through ontologies.

BACKGROUND: Human immunology studies often rely on the isolation and quantification of cell populati...

Novel pediatric-automated respiratory score using physiologic data and machine learning in asthma.

OBJECTIVES: Manual clinical scoring systems are the current standard used for acute asthma clinical ...

Quantitative Prediction of the Landscape of T Cell Epitope Immunogenicity in Sequence Space.

Immunodominant T cell epitopes preferentially targeted in multiple individuals are the critical elem...