Interpretation of microbiota-based diagnostics by explaining individual classifier decisions.

Area of Science:

• Microbiology
• Bioinformatics
• Machine Learning

Background:

• Human microbiota is linked to diseases and diagnostics, but its complex data (high-dimensional, sparse, variable) challenges traditional methods.
• Machine learning (ML) tools are essential for analyzing microbiota data but often function as 'black boxes,' hindering clinical trust and application.
• Interpreting ML classifier decisions in a biologically meaningful context is crucial for reliable clinical use.

Purpose of the Study:

• To develop and validate a method for elucidating microbiota-based classifier decisions.
• To provide biologically meaningful interpretations of ML model predictions for microbiota data.
• To enhance the interpretability and trustworthiness of ML diagnostic tools in clinical microbiology.

Main Methods:

• Applied an explanation method to two microbiota datasets: gut vs. skin and IBD vs. healthy gut.
• Simulated bacterial species as unknown to a pre-trained classifier to measure their impact on classification outcomes.
• Assigned patients unique quantitative estimations of species' contributions to their sample classification.

Main Results:

• The explanation algorithm successfully interpreted classifier decisions on complex microbiota datasets.
• Validation confirmed the accuracy and biological consistency of the explanations with current microbiota research.
• The method provided patient-specific insights into which microbial species drove classification.

Conclusions:

• Explaining individual classifier decisions for complex microbiota analysis is feasible and promising.
• This interpretability method can guide clinical microbiologists and increase confidence in ML-based diagnostic systems.
• Facilitates the development of novel, interpretable diagnostic applications for the human microbiota.