AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

Genome, Viral

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Pathogenicity and virulence of lumpy skin disease virus: A comprehensive update.

Virulence
Lumpy skin disease (LSD), which was confined to the Africa for many decades, has expanded its geographical distribution to numerous countries across Asia and Europe in recent years. The LSD virus (LSDV) is a relatively poorly studied virus. Its 151 K...

VirDetect-AI: a residual and convolutional neural network-based metagenomic tool for eukaryotic viral protein identification.

Briefings in bioinformatics
This study addresses the challenging task of identifying viruses within metagenomic data, which encompasses a broad array of biological samples, including animal reservoirs, environmental sources, and the human body. Traditional methods for virus ide...

Evaluating Neural Network Performance in Predicting Disease Status and Tissue Source of JC Polyomavirus from Patient Isolates Based on the Hypervariable Region of the Viral Genome.

Viruses
JC polyomavirus (JCPyV) establishes a persistent, asymptomatic kidney infection in most of the population. However, JCPyV can reactivate in immunocompromised individuals and cause progressive multifocal leukoencephalopathy (PML), a fatal demyelinatin...

Machine Learning Early Detection of SARS-CoV-2 High-Risk Variants.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has evolved many high-risk variants, resulting in repeated COVID-19 waves over the past years. Therefore, accurate early warning of high-risk variants is vital for epidemic prevention a...

Pathogen genomic surveillance and the AI revolution.

Journal of virology
The unprecedented sequencing efforts during the COVID-19 pandemic paved the way for genomic surveillance to become a powerful tool for monitoring the evolution of circulating viruses. Herein, we discuss how a state-of-the-art artificial intelligence ...

Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study.

BMC infectious diseases
BACKGROUND: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus ...

Unveiling the ghost: machine learning's impact on the landscape of virology.

The Journal of general virology
The complexity and speed of evolution in viruses with RNA genomes makes predictive identification of variants with epidemic or pandemic potential challenging. In recent years, machine learning has become an increasingly capable technology for address...

Using minor variant genomes and machine learning to study the genome biology of SARS-CoV-2 over time.

Nucleic acids research
In infected individuals, viruses are present as a population consisting of dominant and minor variant genomes. Most databases contain information on the dominant genome sequence. Since the emergence of SARS-CoV-2 in late 2019, variants have been sele...

PhageDPO: A machine-learning based computational framework for identifying phage depolymerases.

Computers in biology and medicine
Bacteriophages (phages) are the most predominant and genetically diverse biological entities on Earth. Phages are viruses that infect bacteria and encode numerous proteins with potential biotechnological application. However, most phage-encoded prote...

Predicting viral host codon fitness and path shifting through tree-based learning on codon usage biases and genomic characteristics.

Scientific reports
Viral codon fitness (VCF) of the host and the VCF shifting has seldom been studied under quantitative measurements, although they could be concepts vital to understand pathogen epidemiology. This study demonstrates that the relative synonymous codon ...