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Author Spotlight: Understanding and Detecting Environmental Antimicrobial Resistance by Combining Culture-Based Techniques and Genomics
Published on: July 19, 2024
[Application of Machine Learning Techniques for Antimicrobial Resistance Prediction]
Chao Huang1,2, Lu-Kai Qiao1,2, Yi-Chun Wang1,2
1Guangdong Provincial Key Laboratory of Chemical Pollution and Environmental Safety, Ministry of Education Key Laboratory of Environmental Theoretical Chemistry, SCNU Environmental Research Institute, South China Normal University, Guangzhou 510006, China.
Abstract:
Due to the abuse or excessive use of antimicrobials， particularly antibiotics， antimicrobial resistance （AMR） has become one of the major challenges in global public health. The rapid growth of microbial data， facilitated by advancements in high-throughput sequencing technology， underscores the importance of leveraging machine learning for predicting AMR and identifying resistance markers. Machine learning， encompassing supervised and unsupervised learning， has been proven effective by early studies of AMR prediction. By analyzing microbial genomes and AMR data to build machine learning models， we can improve predictions of microbial resistance and develop more effective antibiotic use strategies， thereby controlling the spread of resistance. This review article focuses on the specific construction processes of machine learning algorithms and the models commonly employed in AMR studies. It also highlights the diverse applications and prospects of machine learning in AMR prediction， with the goal of offering a scientific foundation for future environmental AMR monitoring initiatives.
Insights
Antimicrobial resistance (AMR) is a global health threat. Machine learning models, analyzing microbial data, show promise for predicting AMR and guiding antibiotic strategies to combat resistance.
Area of Science:
- Microbiology
- Bioinformatics
- Computational Biology
Background:
- Antimicrobial resistance (AMR) poses a significant global public health challenge due to the overuse of antimicrobials, especially antibiotics.
- Advancements in high-throughput sequencing have led to a surge in microbial data, necessitating advanced analytical approaches.
Purpose of the Study:
- To review machine learning (ML) algorithms and models used for predicting AMR and identifying resistance markers.
- To explore the applications and future prospects of ML in combating antimicrobial resistance.
Main Methods:
- Analysis of existing literature on machine learning applications in AMR prediction.
- Focus on the construction processes of ML algorithms and commonly employed models in AMR studies.
Main Results:
- Machine learning, including supervised and unsupervised learning, has demonstrated effectiveness in early AMR prediction studies.
- ML models can analyze microbial genomes and AMR data to enhance resistance predictions.
Conclusions:
- Leveraging machine learning is crucial for improving AMR prediction and developing effective antibiotic strategies.
- This review provides a scientific basis for future environmental AMR monitoring initiatives using ML.
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