[Application of Machine Learning Techniques for Antimicrobial Resistance Prediction].

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.