In this study, we developed a digital twin for SARS-CoV-2 by integrating diverse data and metadata with multiple data types and processing strategies, including machine learning, natural language processing, protein structural modeling, and protein s...
This study presents a neural network-based framework for COVID-19 transmission prediction and healthcare resource optimization. The model achieves high prediction accuracy by integrating epidemiological, mobility, vaccination, and environmental data ...
Recently, the advent of Vision Transformer (ViT) has brought substantial advancements in 3D benchmarks, particularly in 3D volumetric medical image segmentation (Vol-MedSeg). Concurrently, multi-layer perceptron (MLP) network has regained popularity ...
BACKGROUND: This study intends to investigate the relationship between artificial intelligence and teachers' collaboration in educational research in response to the growing use of technologies and the current status of the field.
BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.
BACKGROUND: The COVID-19 pandemic has been accompanied by an "infodemic," where the rapid spread of misinformation has exacerbated public health challenges. Traditional fact-checking methods, though effective, are time-consuming and resource-intensiv...
PURPOSE: Programmed cell death (PCD) plays a key role in the progression of coronavirus disease 2019 (COVID-19). However, PCD-relevant biomarkers have not been fully discovered. The aim of this study was to explore the PCD-relevant biomarkers for the...
This study presents a novel approach for the classification of coronavirus species and variants of SARS-CoV-2 using Chaos Game Representation (CGR) and 2D Multifractal Detrended Fluctuation Analysis (2D MF-DFA). By extracting fractal parameters from ...
The COVID-19 outbreak has highlighted the importance of mathematical epidemic models like the Susceptible-Infected-Recovered (SIR) model, for understanding disease spread dynamics. However, enhancing their predictive accuracy complicates parameter es...
Public health practitioners can use wastewater data to grasp disease dynamics, including incidence, prevalence, and potential disease trajectory. The expertise required to analyze and interpret wastewater data exceed those of most entry-level epidemi...
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