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.
Document Type
Editorial
Date of Publication
1-1-2023
Volume
14
Funding Information
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)
School
School of Medical and Health Sciences / Centre for Precision Health
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Publisher
Frontiers
Recommended Citation
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. DOI: https://doi.org/10.3389/fmicb.2023.1203297
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