Nine (not so simple) steps: a practical guide to using machine learning in microbial ecology.

Area of Science:

• Microbial Ecology
• Bioinformatics
• Computational Biology

Background:

• Microbiome data complexity necessitates advanced analytical methods.
• Machine learning (ML) models offer powerful tools for microbial ecology research.
• Existing literature often focuses on ML algorithm performance rather than practical application.

Purpose of the Study:

• To provide actionable guidance for microbial ecologists on selecting and applying ML models.
• To demystify the interpretation of ML model results for microbiome data.
• To address common challenges and best practices in ML for microbial ecology.

Main Methods:

• The review synthesizes current understanding of ML applications in microbial ecology.
• It focuses on practical considerations for data analysis and interpretation.
• Examples and common pitfalls specific to microbiome data are discussed.

Main Results:

• Microbiome data possess unique characteristics requiring tailored ML approaches.
• Careful consideration of ML model selection and interpretation is crucial for accurate predictions.
• The review highlights opportunities and potential pitfalls in applying ML to microbiome datasets.

Conclusions:

• This review equips microbial ecologists with the knowledge to effectively utilize ML for microbiome data analysis.
• It emphasizes practical application and interpretation over theoretical comparisons of ML algorithms.
• The goal is to enhance the predictive power of ML models in microbial ecology research.