By targeting invasive organisms, antibiotics insert themselves into the ancient struggle of the
host-pathogen evolutionary arms race. As pathogens evolve tactics for evading antibiotics …

Background Machine learning (ML) is a growing field in medicine. This narrative review
describes the current body of literature on ML for clinical decision support in infectious …

Artificial intelligence (AI) is a branch of science and engineering that focuses on the
computational understanding of intelligent behavior. Many human professions, including …

Food safety continues to threaten public health. Machine learning holds potential in
leveraging large, emerging data sets to improve the safety of the food supply and mitigate …

The wide use and abuse of antibiotics could make antimicrobial resistance (AMR) an
increasingly serious issue that threatens global health and imposes an enormous burden on …

Machine learning (ML) algorithms are increasingly applied in medical research and in
healthcare, gradually improving clinical practice. Among various applications of these novel …

Antimicrobial resistance (AMR) is emerging as a potential threat to many lives worldwide. It
is very important to understand and apply effective strategies to counter the impact of AMR …

Understanding the relationship between the genome of a cell and its phenotype is a central
problem in precision medicine. Nonetheless, genotype-to-phenotype prediction comes with …

High-throughput DNA sequencing-based approaches continue to revolutionise our
understanding of microbial ecosystems, including those associated with fermented foods …

Background Machine learning (ML) allows the analysis of complex and large data sets and
has the potential to improve health care. The clinical microbiology laboratory, at the interface …