AMR-meta: a k-mer and metafeature approach to classify antimicrobial resistance from high-throughput short-read metagenomics data.

Simone Marini Marco Oliva Ilya B SlizovskiyRishabh A DasNoelle Robertson NoyesTamer KahveciChristina Boucher Mattia A Prosperi

Published in: GigaScience (2022)

AMR-meta is a fast, accurate classifier that exploits non-AMR negative sets to improve sensitivity and specificity. The differences in AMR ontologies and the high variance of all tools in classification outputs call for the deployment of standard benchmarking data and protocols, to fairly compare AMR prediction tools.

Keyphrases

antimicrobial resistance
high throughput
electronic health record
big data
machine learning
high resolution
single cell
data analysis
artificial intelligence