AIMC Topic: Drug Resistance, Bacterial
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Machine learning detection of heteroresistance in Escherichia coli.
EBioMedicine
Feb 21, 2025
BACKGROUND: Heteroresistance (HR) is a significant type of antibiotic resistance observed for several bacterial species and antibiotic classes where a susceptible main population contains small subpopulations of resistant cells. Mathematical models, ...
End-To-End Deep Learning Explains Antimicrobial Resistance in Peak-Picking-Free MALDI-MS Data.
Analytical chemistry
Feb 2, 2025
Mass spectrometry is used to determine infectious microbial species in thousands of clinical laboratories across the world. The vast amount of data allows modern data analysis methods that harvest more information and potentially answer new questions...
Integrating Machine Learning with MALDI-TOF Mass Spectrometry for Rapid and Accurate Antimicrobial Resistance Detection in Clinical Pathogens.
International journal of molecular sciences
Jan 28, 2025
Antimicrobial resistance (AMR) is one of the most pressing public health challenges of the 21st century. This study aims to evaluate the efficacy of mass spectral data generated by VITEK MS instruments for predicting antibiotic resistance in , , and ...
Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.
Veterinary microbiology
Jan 8, 2025
Mannheimia haemolytica is one of the most common causative agents of bovine respiratory disease (BRD); however, antibiotic resistance in this species is increasing, making treatment more difficult. Integrative-conjugative elements (ICE), a subset of ...
ARGai 1.0: A GAN augmented in silico approach for identifying resistant genes and strains in E. coli using vision transformer.
Computational biology and chemistry
Jan 7, 2025
The emergence of infectious disease and antibiotic resistance in bacteria like Escherichia coli (E. coli) shows the necessity for novel computational techniques for identifying essential genes that contribute to resistance. The task of identifying re...
Measuring water pollution effects on antimicrobial resistance through explainable artificial intelligence.
Environmental pollution (Barking, Essex : 1987)
Jan 7, 2025
Antimicrobial resistance refers to the ability of pathogens to develop resistance to drugs designed to eliminate them, making the infections they cause more difficult to treat and increasing the likelihood of disease diffusion and mortality. As such,...
Machine learning and clinician predictions of antibiotic resistance in Enterobacterales bloodstream infections.
The Journal of infection
Dec 30, 2024
BACKGROUND: Patients with Gram-negative bloodstream infections are at risk of serious adverse outcomes without active treatment, but identifying who has antimicrobial resistance (AMR) to target empirical treatment is challenging.
Using genomic data and machine learning to predict antibiotic resistance: A tutorial paper.
PLoS computational biology
Dec 30, 2024
Antibiotic resistance is a global public health concern. Bacteria have evolved resistance to most antibiotics, which means that for any given bacterial infection, the bacteria may be resistant to one or several antibiotics. It has been suggested that...
A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.
International journal of medical informatics
Dec 7, 2024
This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificial intellig...
Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.
BMC infectious diseases
Dec 5, 2024
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...
Streptomycin
Mycobacterium tuberculosis
Isoniazid
Rifampin
Humans
Microbial Sensitivity Tests
Uganda
Polymorphism, Single Nucleotide
Tuberculosis, Multidrug-Resistant
Whole Genome Sequencing
Antitubercular Agents
South Africa
Young Adult
Adult
Male
Middle Aged
Machine Learning
Drug Resistance, Bacterial
Female
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