Towards multi-label classification: Next step of machine learning for microbiome research.

Area of Science:

• Microbiome research
• Computational biology
• Host-microbe interactions

Background:

• Machine learning (ML) is crucial for identifying microbial biomarkers and predicting diseases from microbiome data.
• Current ML models typically use single-label classification, assuming one disease per sample.
• Real-world scenarios involve individuals with multiple co-occurring diseases (comorbidities), complicating microbial pattern analysis.

Purpose of the Study:

• To review standard single-label ML classification methods in microbiome studies.
• To highlight the limitations of these methods in detecting multiple diseases simultaneously.
• To introduce multi-label classification as a solution for complex disease states in microbiome research.

Main Methods:

• Review of existing literature on single-label ML in microbiome analysis.
• Demonstration of single-label ML limitations using a real-world dataset with multi-label disease information.
• Exploration of multi-label classification strategies and associated technical challenges.

Main Results:

• Single-label ML models struggle to accurately classify disease status when multiple conditions are present.
• Comorbidities introduce variations in microbial profiles that standard models cannot effectively handle.
• Multi-label classification offers a promising approach to overcome these limitations.

Conclusions:

• Current ML approaches in microbiome research are insufficient for individuals with multiple diseases.
• Adopting multi-label classification is essential for advancing the practical application of microbiome analysis in complex clinical settings.
• Further research into multi-label strategies is needed to fully leverage microbiome data for comprehensive health assessment.