Allergy & Immunology

Machine learning in asthma research: moving toward a more integrated approach.

: Big data are reshaping the future of medicine. The growing availability and increasing complexity of data have favored the adoption of modern analytical and computational methodologies in every area of medicine. Over the past decades, asthma research has been characterized by a shift in the way studies are conducted and data are analyzed. Motivated by the assumptions that 'data will speak for th...

Automated severity scoring of atopic dermatitis patients by a deep neural network.

Scoring atopic dermatitis (AD) severity with the Eczema Area and Severity Index (EASI) in an objective and reproducible manner is challenging. Automated measurement of erythema, papulation, excoriation, and lichenification severity using images has not yet been investigated. Our aim was to determine whether convolutional neural networks (CNNs) could assess erythema, papulation, excoriation, and li...

Extract Extenuates Venom-Induced Toxicities, Histopathological Impairments and Inflammation via Enhancement of Nrf2 Expression in Rats.

snakebite causes more fatalities than all other African snake species combined. reportedly possesses an antivenom property. Therefore, we evaluated ...

Personalized prediction of early childhood asthma persistence: A machine learning approach.

Early childhood asthma diagnosis is common; however, many children diagnosed before age 5 experience symptom resolution and it remains difficult to id...

Trends in application of advancing computational approaches in GPCR ligand discovery.

G protein-coupled receptors (GPCRs) comprise the most important superfamily of protein targets in current ligand discovery and drug development. GPCRs...

Explainable AI reveals changes in skin microbiome composition linked to phenotypic differences.

Alterations in the human microbiome have been observed in a variety of conditions such as asthma, gingivitis, dermatitis and cancer, and much remains ...

Accuracy of Algorithms to Identify People with Atopic Dermatitis in Ontario Routinely Collected Health Databases.

TAP 1.0: A robust immunoinformatic tool for the prediction of tumor T-cell antigens based on AAindex properties.

Immunotherapy is a research area with great potential in drug discovery for cancer treatment. Because of the capacity of tumor antigens to activate th...

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the ...

Classification models and SAR analysis on CysLT1 receptor antagonists using machine learning algorithms.

Cysteinyl leukotrienes 1 (CysLT1) receptor is a promising drug target for rhinitis or other allergic diseases. In our study, we built classification m...

AllergyMap: An Open Source Corpus of Allergy Mention Normalizations.

Allergy mention normalization is challenging because of the wide range of possible allergens including medications, foods, plants, animals, and consum...

Asthma-prone areas modeling using a machine learning model.

Nowadays, owing to population growth, increasing environmental pollution, and lifestyle changes, the number of asthmatics has significantly increased....

Automated detection of mouse scratching behaviour using convolutional recurrent neural network.

Scratching is one of the most important behaviours in experimental animals because it can reflect itching and/or psychological stress. Here, we aimed ...

Novel Machine Learning Can Predict Acute Asthma Exacerbation.

BACKGROUND: Asthma exacerbations result in significant health and economic burden, but are difficult to predict.

Performance improvement of machine learning techniques predicting the association of exacerbation of peak expiratory flow ratio with short term exposure level to indoor air quality using adult asthmatics clustered data.

Large-scale data sources, remote sensing technologies, and superior computing power have tremendously benefitted to environmental health study. Recent...

MATHLA: a robust framework for HLA-peptide binding prediction integrating bidirectional LSTM and multiple head attention mechanism.

BACKGROUND: Accurate prediction of binding between class I human leukocyte antigen (HLA) and neoepitope is critical for target identification within p...

Effects of Neurodynamic Mobilizations on Pain Hypersensitivity in Patients With Hand Osteoarthritis Compared to Robotic Assisted Mobilization: A Randomized Controlled Trial.

OBJECTIVE: To evaluate the effectiveness of the neurodynamic mobilization techniques compared with passive robotic physiologic movement in patients wi...

Quantitative analysis of blood cells from microscopic images using convolutional neural network.

Blood cell count provides relevant clinical information about different kinds of disorders. Any deviation in the number of blood cells implies the pre...

Assessing PD-L1 expression in non-small cell lung cancer and predicting responses to immune checkpoint inhibitors using deep learning on computed tomography images.

This study aimed to use computed tomography (CT) images to assess PD-L1 expression in non-small cell lung cancer (NSCLC) and predict response to immu...

Semi-supervised learning for somatic variant calling and peptide identification in personalized cancer immunotherapy.

BACKGROUND: Personalized cancer vaccines are emerging as one of the most promising approaches to immunotherapy of advanced cancers. However, only a sm...