Research Area:  Machine Learning

Abstract:

Antimicrobial resistance extracts high morbidity, mortality and economic costs yearly by rendering bacteria immune to antibiotics. Identifying and understanding antimicrobial resistance are imperative for clinical practice to treat resistant infections and for public health efforts to limit the spread of resistance. Technologies such as next-generation sequencing are expanding our abilities to detect and study antimicrobial resistance. This Review provides a detailed overview of antimicrobial resistance identification and characterization methods, from traditional antimicrobial susceptibility testing to recent deep-learning methods. We focus on sequencing-based resistance discovery and discuss tools and databases used in antimicrobial resistance studies.

Keywords:  
Antimicrobial resistance
Infectious diseases
Metagenomics
Microbial genetics
Next-generation sequencing

Author(s) Name:  volume 2

Publisher name:  Manish Boolchandani, Alaric W. D-Souza & Gautam Dantas

DOI:  10.1038/s41576-019-0108-4

Volume Information:  volume 20

Paper Link:   https://www.nature.com/articles/s41576-019-0108-4