AIMC Topic: Genome, Bacterial

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

Canadian journal of microbiology Jul 30, 2024
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)...
Enterococcus faecium Plasmids Gram-Positive Bacterial Infections Genome, Bacterial Machine Learning Anti-Bacterial Agents Drug Resistance, Bacterial Microbial Sensitivity Tests Bacterial Proteins Enterococcus faecalis Humans
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Differentially used codons among essential genes in bacteria identified by machine learning-based analysis.

Molecular genetics and genomics : MGG Jul 27, 2024
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...
Machine Learning Bacteria Codon Escherichia coli Genome, Bacterial Codon Usage Genes, Essential Genes, Bacterial
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Characterization of the prevalence of Salmonella in different retail chicken supply modes using genome-wide and machine-learning analyses.

Food research international (Ottawa, Ont.) Jun 19, 2024
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...
Whole Genome Sequencing Serogroup Genome, Bacterial Chickens Food Microbiology Meat Anti-Bacterial Agents Machine Learning Prevalence Drug Resistance, Bacterial Animals China Salmonella
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Protein function annotation and virulence factor identification of Klebsiella pneumoniae genome by multiple machine learning models.

Microbial pathogenesis Jun 6, 2024
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...
Computational Biology Proteome Klebsiella Infections Humans Machine Learning Klebsiella pneumoniae Genome, Bacterial Bacterial Proteins Sequence Alignment Virulence Factors Molecular Sequence Annotation
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Predicting S. aureus antimicrobial resistance with interpretable genomic space maps.

Molecular informatics Feb 22, 2024
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...
Staphylococcus aureus Genome, Bacterial Drug Resistance, Bacterial Genomics Humans Machine Learning Anti-Bacterial Agents
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Operon Finder: A Deep Learning-based Web Server for Accurate Prediction of Prokaryotic Operons.

Journal of molecular biology Dec 14, 2022
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...
Genome, Bacterial Operon Prokaryotic Cells Deep Learning Humans Sequence Analysis, DNA Software Internet Algorithms
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Deep learning of a bacterial and archaeal universal language of life enables transfer learning and illuminates microbial dark matter.

Nature communications May 11, 2022
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...
Genome, Archaeal Deep Learning Archaea Genome, Bacterial Bacteria Sequence Analysis Proteins
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Deep Learning to Predict the Biosynthetic Gene Clusters in Bacterial Genomes.

Journal of molecular biology May 6, 2022
Biosynthetic gene clusters (BGCs) in bacterial genomes code for important small molecules and secondary metabolites. Based on the validated BGCs and the corresponding sequences of protein family domains (Pfams), Pfam functions and clan information, w...
Deep Learning Bacteria Multigene Family Biosynthetic Pathways Genome, Bacterial
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Phenotype-Based Threat Assessment.

Proceedings of the National Academy of Sciences of the United States of America Apr 1, 2022
Bacterial pathogen identification, which is critical for human health, has historically relied on culturing organisms from clinical specimens. More recently, the application of machine learning (ML) to whole-genome sequences (WGSs) has facilitated pa...
Machine Learning Phenotype Virulence Genome, Bacterial Bacteria Whole Genome Sequencing Virulence Factors
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Accurate and rapid prediction of tuberculosis drug resistance from genome sequence data using traditional machine learning algorithms and CNN.

Scientific reports Feb 14, 2022
Effective and timely antibiotic treatment depends on accurate and rapid in silico antimicrobial-resistant (AMR) predictions. Existing statistical rule-based Mycobacterium tuberculosis (MTB) drug resistance prediction methods using bacterial genomic s...
Genome, Bacterial Microbial Sensitivity Tests Drug Resistance, Multiple, Bacterial Whole Genome Sequencing Cohort Studies Mycobacterium tuberculosis Mutation Data Accuracy Antitubercular Agents Prognosis Tuberculosis, Multidrug-Resistant Humans Phylogeny Phenotype Deep Learning
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