Microbial Primer: Artificial intelligence for microbiologists.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• Artificial intelligence (AI) and machine learning (ML) are increasingly influential in scientific research.
• Microbiology benefits from AI/ML for tasks like antibiotic discovery, resistance prediction, and diagnostics.
• A practical approach is needed for microbiologists to effectively utilize ML without needing to build algorithms from scratch.

Purpose of the Study:

• To provide a practical primer for microbiologists on applying machine learning (ML) in their field.
• To guide researchers on selecting appropriate ML methods, preparing data, and interpreting results responsibly.
• To highlight the potential of ML in antibiotic discovery, microbiome studies, and pathogen genomics.

Main Methods:

• ML models learn patterns from complex and often noisy biological data.
• Supervised learning models are used to map sequence features to outcomes like antimicrobial susceptibility.
• Unsupervised learning methods facilitate exploration, clustering, and dimensionality reduction for visualization.

Main Results:

• ML aids in identifying and designing novel antibiotic compounds using generative AI.
• In microbiome studies, ML helps uncover community structures and link microbial taxa to phenotypes.
• ML accurately predicts species identity, antimicrobial susceptibility, and MIC from pathogen genomic data.

Conclusions:

• Effective ML application in microbiology depends on sound problem framing, data preprocessing, and experimental validation.
• ML offers transformative potential across various microbiology domains, from drug discovery to diagnostics.
• Microbiologists can leverage ML by understanding its appropriate use and responsible interpretation rather than algorithm development.