IMPACT: interpretable microbial phenotype analysis via microbial characteristic traits.

Area of Science:

• Microbiology
• Bioinformatics
• Computational Biology

Background:

• The human gut microbiome plays a crucial role in health and disease.
• Advanced profiling technologies offer insights into microbiome-disease links.
• Current models lack interpretability, hindering deeper understanding of the microbiome's role in disease.

Purpose of the Study:

• To develop a novel framework for analyzing the gut microbiome that enhances interpretability.
• To improve the understanding of the relationship between microbial features and complex disease outcomes.

Main Methods:

• A framework combining feature engineering to convert tabular abundance data into image format using functional microbial annotation databases.
• Utilizing a residual spatial attention transformer architecture for phenotype classification.

Main Results:

• The IMPACT model achieved superior predictive accuracy in multiclass classification compared to existing methods.
• The approach provides interpretable feature importance via image classification saliency methods.
• Enabled extraction of specific taxa markers, functional traits, and metabolites associated with disease outcomes.

Conclusions:

• The IMPACT framework offers a powerful tool for microbiome research, improving both predictive accuracy and interpretability.
• This work facilitates a deeper understanding of the gut microbiome's contribution to disease.
• IMPACT is publicly available for direct installation via pip.