Asthma

Quantum Mechanics and Machine Learning Synergies: Graph Attention Neural Networks to Predict Chemical Reactivity.

There is a lack of scalable quantitative measures of reactivity that cover the full range of functional groups in organic chemistry, ranging from highly unreactive C-C bonds to highly reactive naked ions. Measuring reactivity experimentally is costly and time-consuming, and no single method has sufficient dynamic range to cover the astronomical size of chemical reactivity space. In previous quantu...

Unsupervised learning of brain state dynamics during emotion imagination using high-density EEG.

This study applies adaptive mixture independent component analysis (AMICA) to learn a set of ICA models, each optimized by fitting a distributional model for each identified component process while maximizing component process independence within some subsets of time points of a multi-channel EEG dataset. Here, we applied 20-model AMICA decomposition to long-duration (1-2 h), high-density (128-cha...

Evaluation of Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction Based on CT Features of Deep Learning.

This study was aimed at exploring the treatment of asthma children with small airway obstruction in CT imaging features of deep learning and glucocort...

Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations.

Artificial intelligence (AI) systems have increasingly achieved expert-level performance in medical imaging applications. However, there is growing co...

Predictive models for personalized asthma attacks based on patient's biosignals and environmental factors: a systematic review.

BACKGROUND: Asthma is a chronic disease that exacerbates due to various risk factors, including the patient's biosignals and environmental conditions....

Identifying Microbe-Disease Association Based on a Novel Back-Propagation Neural Network Model.

Over the years, numerous evidences have demonstrated that microbes living in the human body are closely related to human life activities and human dis...

Drug repurposing for COVID-19 using graph neural network and harmonizing multiple evidence.

Since the 2019 novel coronavirus disease (COVID-19) outbreak in 2019 and the pandemic continues for more than one year, a vast amount of drug research...

A ferret brain slice model of oxygen-glucose deprivation captures regional responses to perinatal injury and treatment associated with specific microglial phenotypes.

Organotypic brain slice models are an ideal technological platform to investigate therapeutic options for hypoxic-ischemic (HI) brain injury, a leadin...

Advanced Recovery and High-Sensitive Properties of Memristor-Based Gas Sensor Devices Operated at Room Temperature.

Fast recovery, high sensitivity, high selectivity, and room temperature (RT) sensing characteristics of NO gas sensors are essential for environmental...

Identification of asthma control factor in clinical notes using a hybrid deep learning model.

BACKGROUND: There are significant variabilities in guideline-concordant documentation in asthma care. However, assessing clinician's documentation is ...

Machine learning implicates the IL-18 signaling axis in severe asthma.

Asthma is a common disease with profoundly variable natural history and patient morbidity. Heterogeneity has long been appreciated, and much work has ...

Recurrent Wheeze Exacerbations Following Acute Bronchiolitis-A Machine Learning Approach.

Acute bronchiolitis is one of the most common respiratory infections in infancy. Although most infants with bronchiolitis do not get hospitalized, in...

Deep Learning Algorithms-Based CT Images in Glucocorticoid Therapy in Asthma Children with Small Airway Obstruction.

CT image information data under deep learning algorithms was adopted to evaluate small airway function and analyze the clinical efficacy of different ...

DMFMDA: Prediction of Microbe-Disease Associations Based on Deep Matrix Factorization Using Bayesian Personalized Ranking.

Identifying the microbe-disease associations is conducive to understanding the pathogenesis of disease from the perspective of microbe. In this paper,...

Allergen Risk Assessment for Specific Allergy to Small Ruminant's Milk: Development of Sensitive Immunoassays to Detect Goat's and Sheep's Milk Contaminations in Dairy Food Matrices.

Despite a high level of sequence identity between cow's, goat's, and sheep's milk (CM, GM, and SM, respectively) proteins, some patients tolerant to C...

Applying interpretable deep learning models to identify chronic cough patients using EHR data.

BACKGROUND AND OBJECTIVE: Chronic cough (CC) affects approximately 10% of adults. Many disease states are associated with chronic cough, such as asthm...

NIgPred: Class-Specific Antibody Prediction for Linear B-Cell Epitopes Based on Heterogeneous Features and Machine-Learning Approaches.

Upon invasion by foreign pathogens, specific antibodies can identify specific foreign antigens and disable them. As a result of this ability, antibodi...

Artificial intelligence-assisted clinical decision support for childhood asthma management: A randomized clinical trial.

RATIONALE: Clinical decision support (CDS) tools leveraging electronic health records (EHRs) have been an approach for addressing challenges in asthma...

Explainable deep learning predictions for illness risk of mental disorders in Nanjing, China.

Epidemiological studies have revealed the associations of air pollutants and meteorological factors with a range of mental health conditions. However,...

DBP-GAPred: An intelligent method for prediction of DNA-binding proteins types by enhanced evolutionary profile features with ensemble learning.

DNA-binding proteins (DBPs) perform an influential role in diverse biological activities like DNA replication, slicing, repair, and transcription. Som...