Machine learning in applied microbiology, from data quality to model validation and implementation.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Machine learning (ML) is increasingly used in microbiology, but its application and reliability vary significantly across different research areas.
• Evaluating ML performance requires a framework assessing data readiness, model suitability, and deployment readiness.

Purpose of the Study:

• To review and analyze the application and reliability of machine learning (ML) in diverse microbiological domains.
• To identify key factors influencing ML performance and practical utility in areas such as diagnostics, virology, and biotechnology.

Main Methods:

• Systematic review of 254 scientific articles evaluating ML in microbiology.
• Analysis framework based on data readiness, model suitability, and deployment readiness.
• Synthesis of quantitative benchmarks comparing different ML approaches and their applications.

Main Results:

• Pathogen identification and antimicrobial resistance prediction show strong performance with curated data but face limitations in validation and transferability.
• Virology studies are challenged by incomplete databases and taxonomic changes; microbiome research struggles with generalization due to data structure and metadata.
• Industrial and environmental applications show promise in controlled settings but limited deployment.
• Classical supervised models are competitive with deep learning in interpretability and validation for structured data.

Conclusions:

• Advancements in ML for microbiology depend more on data quality, standardization, and validation than algorithmic innovation.
• Interoperable datasets, representative sampling, standardized benchmarking, and reproducible workflows are crucial for reliable ML deployment.
• Prospective multi-site validation is essential to confirm the practical value of ML tools across different settings.