AIMC Topic: Genome, Bacterial

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Pato: prediction of probiotic bacteria using metabolic features.

Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology]
Probiotics have gained recognition for their health-promoting benefits, particularly in the gastrointestinal and immunological systems. Among promising probiotic candidates, Lactobacillus strains, belonging to the lactic acid bacteria (LAB) group, pl...

Subtractive genomics approach: A guide to unveiling therapeutic targets across pathogens.

Journal of microbiological methods
Subtractive genomics is an adaptable bioinformatics technique that is used to identify potential therapeutic targets by differentiating essential genes in pathogens and non-pathogenic genes. Since, identification of therapeutic targets and understand...

Integrating Deep Learning Models with Genome-Wide Association Study-Based Identification Enhanced Phenotype Predictions in Group A .

Journal of microbiology and biotechnology
Group A (GAS) is a major pathogen with diverse clinical outcomes linked to its genetic variability, making accurate phenotype prediction essential. While previous studies have identified many GAS-associated genetic factors, translating these finding...

Quantitative prediction of disinfectant tolerance in Listeria monocytogenes using whole genome sequencing and machine learning.

Scientific reports
Listeria monocytogenes is a potentially severe disease-causing bacteria mainly transmitted through food. This pathogen is of great concern for public health and the food industry in particular. Many countries have implemented thorough regulations, an...

Assessment for antibiotic resistance in : A practical and interpretable machine learning model based on genome-wide genetic variation.

Virulence
() antibiotic resistance poses a global health threat. Accurate identification of antibiotic resistant strains is essential for the control of infection. In the present study, our goal is to leverage the whole-genome data of to develop practical an...

ListPred: A predictive ML tool for virulence potential and disinfectant tolerance in Listeria monocytogenes.

Infection, genetics and evolution : journal of molecular epidemiology and evolutionary genetics in infectious diseases
Despite current surveillance and sanitation strategies, foodborne pathogens continue to threaten the food industry and public health. Whole genome sequencing (WGS) has reached an unprecedented resolution to analyse and compare pathogenic bacterial is...

deep-Sep: a deep learning-based method for fast and accurate prediction of selenoprotein genes in bacteria.

mSystems
Selenoproteins are a special group of proteins with major roles in cellular antioxidant defense. They contain the 21st amino acid selenocysteine (Sec) in the active sites, which is encoded by an in-frame UGA codon. Compared to eukaryotes, identificat...

Machine learning detection of heteroresistance in Escherichia coli.

EBioMedicine
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, ...

Machine learning reveals the dynamic importance of accessory sequences for outbreak clustering.

mBio
UNLABELLED: Bacterial typing at whole-genome scales is now feasible owing to decreasing costs in high-throughput sequencing and the recent advances in computation. The unprecedented resolution of whole-genome typing is achieved by genotyping the vari...

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 ...