A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.

Area of Science:

• Microbiome research
• Computational biology
• Host-microbe interactions

Background:

• Host health is increasingly linked to variations in microbial communities.
• Advancements in microbiome sequencing and machine learning offer new tools for disease risk prediction.
• Taxonomy-informed feature selection is a key aspect of microbiome-based prediction.

Purpose of the Study:

• To review commonly used machine learning methods for microbiome host trait prediction.
• To evaluate the prediction accuracy of these machine learning methods.
• To provide an accessible overview for non-experts, including R/Python code.

Main Methods:

• Exploration of prevalent machine learning algorithms.
• Evaluation of prediction accuracy using microbiome datasets.
• Description of methods at an introductory level.

Main Results:

• Machine learning methods show promise in predicting host traits from microbiome data.
• Taxonomy-informed feature selection can enhance prediction accuracy.
• The review provides practical code examples for applying these methods.

Conclusions:

• Machine learning is a valuable tool for interpreting complex microbiome data.
• Accurate microbiome-based prediction can contribute to personalized medicine and disease prevention.
• Accessible resources are crucial for advancing microbiome research and application.