AIMC Topic: Genes, Bacterial

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Rapid key gene discovery for bacterial shape: a cross-species machine learning approach.

BMC microbiology
Accurately identifying genes responsible for specific functions is a cornerstone of biological research, but current methods are often limited to single-species analyses. Here, we present a novel method, called Genomic and Phenotype-based machine lea...

High pollution and health risk of antibiotic resistance genes in rural domestic sewage in southeastern China: A study combining national-scale distribution and machine learning.

Environmental pollution (Barking, Essex : 1987)
Rural domestic sewage has emerged as an important reservoir of antibiotic resistance genes (ARGs) under rapid urbanization, while the national-scale geographical patterns and risks of ARGs remaining unclear. We investigated ARG pollution in rural dom...

Influence of microplastics on antibiotic resistance genes across diverse environments: A comprehensive meta and machine-learning analysis.

Journal of hazardous materials
The coexistence of microplastics (MPs) and antibiotic resistance genes (ARGs) in various environments presents significant ecological risks. However, the influence of MPs properties and environmental conditions shaping ARG dynamics remain unclear. Th...

Synergistic effect of horizontal transfer of antibiotic resistance genes between bacteria exposed to microplastics and per/polyfluoroalkyl substances: An explanation from theoretical methods.

Journal of hazardous materials
Microplastics (MPs) and per/polyfluoroalkyl substances (PFASs), as emerging pollutants widely present in aquatic environments, pose a significant threat to human health through the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs)....

A framework predicting removal efficacy of antibiotic resistance genes during disinfection processes with machine learning.

Journal of hazardous materials
Disinfection has been applied widely for the removal of antibiotic resistance genes (ARGs) to curb the spread of antibiotic resistance. Quantitative polymerase chain reaction (qPCR) is the most used method to quantify the damage of DNA thus calculati...

DRAMMA: a multifaceted machine learning approach for novel antimicrobial resistance gene detection in metagenomic data.

Microbiome
BACKGROUND: Antibiotics are essential for medical procedures, food security, and public health. However, ill-advised usage leads to increased pathogen resistance to antimicrobial substances, posing a threat of fatal infections and limiting the benefi...

Phenotypic antibiotic resistance prediction using antibiotic resistance genes and machine learning models in Mannheimia haemolytica.

Veterinary microbiology
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 ...

Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.

Molecular genetics and genomics : MGG
Codon usage bias (CUB), the uneven usage of synonymous codons encoding the same amino acid, differs among genes within and across bacteria genomes. CUB is known to be influenced by gene expression and accordingly, CUB differs between the high-express...

ARGNet: using deep neural networks for robust identification and classification of antibiotic resistance genes from sequences.

Microbiome
BACKGROUND: Emergence of antibiotic resistance in bacteria is an important threat to global health. Antibiotic resistance genes (ARGs) are some of the key components to define bacterial resistance and their spread in different environments. Identific...

Novel candidate genes for environmental stresses response in Synechocystis sp. PCC 6803 revealed by machine learning algorithms.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
Cyanobacteria have developed acclimation strategies to adapt to harsh environments, making them a model organism. Understanding the molecular mechanisms of tolerance to abiotic stresses can help elucidate how cells change their gene expression patter...