AIMC Topic: SARS-CoV-2

Clear Filters Showing 491 to 500 of 1734 articles

Homeostasis imbalance process ontology: a study on COVID-19 infectious processes.

BMC medical informatics and decision making
BACKGROUND: One significant challenge in addressing the coronavirus disease 2019 (COVID-19) pandemic is to grasp a comprehensive picture of its infectious mechanisms. We urgently need a consistent framework to capture the intricacies of its complicat...

COVID‑19 detection from chest X-ray images using transfer learning.

Scientific reports
COVID-19 is a kind of coronavirus that appeared in China in the Province of Wuhan in December 2019. The most significant influence of this virus is its very highly contagious characteristic which may lead to death. The standard diagnosis of COVID-19 ...

PrCRS: a prediction model of severe CRS in CAR-T therapy based on transfer learning.

BMC bioinformatics
BACKGROUND: CAR-T cell therapy represents a novel approach for the treatment of hematologic malignancies and solid tumors. However, its implementation is accompanied by the emergence of potentially life-threatening adverse events known as cytokine re...

Development of a long noncoding RNA-based machine learning model to predict COVID-19 in-hospital mortality.

Nature communications
Tools for predicting COVID-19 outcomes enable personalized healthcare, potentially easing the disease burden. This collaborative study by 15 institutions across Europe aimed to develop a machine learning model for predicting the risk of in-hospital m...

A complex fuzzy decision model for analysing the post-pandemic immuno-sustainability.

Acta tropica
The post-effects of the COronaVIrus Disease (COVID-19) vary depending on socioeconomic and biological factors. Similarly, the effects of vaccination on people's immunity vary across several factors. After the pandemic, real-life post-vaccination anom...

Random forest models of food safety behavior during the COVID-19 pandemic.

International journal of environmental health research
Machine learning approaches are increasingly being adopted as data analysis tools in scientific behavioral predictions. This paper utilizes a machine learning approach, Random Forest Model, to determine the top prediction variables of food safety beh...

Enhancing multi-class lung disease classification in chest x-ray images: A hybrid manta-ray foraging volcano eruption algorithm boosted multilayer perceptron neural network approach.

Network (Bristol, England)
One of the most used diagnostic imaging techniques for identifying a variety of lung and bone-related conditions is the chest X-ray. Recent developments in deep learning have demonstrated several successful cases of illness diagnosis from chest X-ray...

Accelerating reliable multiscale quantum refinement of protein-drug systems enabled by machine learning.

Nature communications
Biomacromolecule structures are essential for drug development and biocatalysis. Quantum refinement (QR) methods, which employ reliable quantum mechanics (QM) methods in crystallographic refinement, showed promise in improving the structural quality ...

Workout Classification Using a Convolutional Neural Network in Ensemble Learning.

Sensors (Basel, Switzerland)
To meet the increased demand for home workouts owing to the COVID-19 pandemic, this study proposes a new approach to real-time exercise posture classification based on the convolutional neural network (CNN) in an ensemble learning system. By utilizin...

Algorithms for predicting COVID outcome using ready-to-use laboratorial and clinical data.

Frontiers in public health
The pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is an emerging crisis affecting the public health system. The clinical features of COVID-19 can range from an asymptomatic state to acute respiratory syndrome and mul...