AIMC Topic: Genome, Bacterial

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Prediction of antimicrobial resistance of Klebsiella pneumoniae from genomic data through machine learning.

PloS one
Antimicrobials, such as antibiotics or antivirals are medications employed to prevent and treat infectious diseases in humans, animals, and plants. Antimicrobial Resistance occurs when bacteria, viruses, and parasites no longer respond to these medic...

Machine learning to attribute the source of Campylobacter infections in the United States: A retrospective analysis of national surveillance data.

The Journal of infection
OBJECTIVES: Integrating pathogen genomic surveillance with bioinformatics can enhance public health responses by identifying risk and guiding interventions. This study focusses on the two predominant Campylobacter species, which are commonly found in...

Identification of key drivers of antimicrobial resistance in using machine learning.

Canadian journal of microbiology
With antimicrobial resistance (AMR) rapidly evolving in pathogens, quick and accurate identification of genetic determinants of phenotypic resistance is essential for improving surveillance, stewardship, and clinical mitigation. Machine learning (ML)...

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...

Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.

Food research international (Ottawa, Ont.)
Salmonella is a foodborne pathogen that causes salmonellosis, of which retail chicken meat is a major source. However, the prevalence of Salmonella in different retail chicken supply modes and the threat posed to consumers remains unclear. The preval...

Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.

Microbial pathogenesis
Klebsiella pneumoniae is a type of Gram-negative bacterium which can cause a range of infections in human. In recent years, an increasing number of strains of K. pneumoniae resistant to multiple antibiotics have emerged, posing a significant threat t...

Predicting S. aureus antimicrobial resistance with interpretable genomic space maps.

Molecular informatics
Increasing antimicrobial resistance (AMR) represents a global healthcare threat. To decrease the spread of AMR and associated mortality, methods for rapid selection of optimal antibiotic treatment are urgently needed. Machine learning (ML) models bas...

Operon Finder: A Deep Learning-based Web Server for Accurate Prediction of Prokaryotic Operons.

Journal of molecular biology
Operons are groups of consecutive genes that transcribe together under the regulation of a common promoter. They influence protein regulation and various physiological pathways, making their accurate detection desirable. The detection of operons thro...

Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter.

Nature communications
The majority of microbial genomes have yet to be cultured, and most proteins identified in microbial genomes or environmental sequences cannot be functionally annotated. As a result, current computational approaches to describe microbial systems rely...