Artificial intelligence-based predictions have emerged as a friendly and reliable tool for the
surveillance of the antimicrobial resistance (AMR) worldwide. In this regard, genome …

Thanks to the genomics revolution, thousands of strain-specific whole-genome sequences
are now accessible for a wide range of pathogenic bacteria. This availability enables big …

Salmonella spp., a leading cause of foodborne illness, is a formidable global menace due to
escalating antimicrobial resistance (AMR). The evaluation of minimum inhibitory …

Motivation Antimicrobial resistance (AMR) is one of the biggest global problems threatening
human and animal health. Rapid and accurate AMR diagnostic methods are thus very …

Nontyphoidal Salmonella species are the leading bacterial cause of food-borne disease in
the United States. Whole genome sequences and paired antimicrobial susceptibility data …

The advent of rapid whole-genome sequencing has created new opportunities for
computational prediction of antimicrobial resistance (AMR) phenotypes from genomic data …

Nontyphoidal Salmonella (NTS) is a leading global cause of bacterial foodborne morbidity
and mortality. Our ability to treat severe NTS infections has been impaired by increasing …

A growing number of studies are using machine learning models to accurately predict
antimicrobial resistance (AMR) phenotypes from bacterial sequence data. Although these …

Realising the promise of genomics to revolutionise routine AMR diagnosis and surveillance
has been a long-standing challenge in clinical and public health microbiology. We have …

Whole-genome sequencing (WGS) is used increasingly in public-health laboratories for
typing and characterizing foodborne pathogens. To evaluate the performance of existing …