Editorial: Computational predictions, dynamic tracking, and evolutionary analysis of antibiotic resistance through the mining of microbial genomes and metagenomic data, volume II

Authors

Qi Zhao
Jian Li
Alfred Tay
Liang Wang, Edith Cowan UniversityFollow

Document Type

Editorial

Publication Title

Frontiers in Microbiology

Volume

14

Publisher

Frontiers

School

School of Medical and Health Sciences / Centre for Precision Health

Funders

Guangdong Basic and Applied Basic Research Foundation (Grant No. 2022A1515220023) / Jiangsu Qinglan Project (Year 2020) / Foundation of Education Department of Liaoning Province (Grant No. LJKZ0280) / Natural Science Foundation of Liaoning Province (Grant No. 2023-MS-288)

Comments

Zhao, Q., Li, J., Tay, A., & Wang, L. (2023). Editorial: Computational predictions, dynamic tracking, and evolutionary analysis of antibiotic resistance through the mining of microbial genomes and metagenomic data, volume II. Frontiers in Microbiology, 14, Article 1203297. https://doi.org/10.3389/fmicb.2023.1203297

Abstract

Antibiotic resistance has emerged as a critical global health challenge, posing a significant threat to human and animal health. The rapid development of antibiotic resistance among pathogens has reduced the efficacy of existing treatments, leading to an urgent need for novel therapeutic strategies. One promising avenue of research involves mining microbial genomes and metagenomic data to uncover novel antimicrobial agents and a better understanding of the resistance mechanism.

DOI

10.3389/fmicb.2023.1203297

Creative Commons License

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