Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data

Machine learning and feature extraction for rapid antimicrobial resistance prediction of Acinetobacter baumannii from whole-genome sequencing data

BackgroundWhole-genome sequencing (WGS) has contributed significantly to advancements in machine learning methods for predicting antimicrobial resistance (AMR). However, the comparisons of different methods for AMR prediction without requiring prior knowledge of resistance remains to be conducted.Me...

Full description

Bibliographic Details
Main Authors: Yue Gao, Henan Li, Chunjiang Zhao, Shuguang Li, Guankun Yin, Hui Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2024-01-01
Series:Frontiers in Microbiology
Subjects:
Acinetobacter baumannii
whole-genome sequencing
antimicrobial resistance prediction
machine learning
k-mer
Online Access:https://www.frontiersin.org/articles/10.3389/fmicb.2023.1320312/full

Internet

https://www.frontiersin.org/articles/10.3389/fmicb.2023.1320312/full

Similar Items