Interpreting Microbiome Relative Abundance Data Using Symbolic Regression

TL;DR

This study applies symbolic regression to microbiome data related to colorectal cancer, demonstrating that it offers comparable predictive performance to traditional models while providing superior interpretability for biological insights.

Contribution

The paper introduces the use of symbolic regression for microbiome data analysis, highlighting its interpretability and ability to elucidate biological relationships in colorectal cancer.

Findings

SR competes well with traditional models in predictive accuracy

SR provides explicit mathematical expressions for biological insights

SR helps interpret complex models like XGBoost

Abstract

Understanding the complex interactions within the microbiome is crucial for developing effective diagnostic and therapeutic strategies. Traditional machine learning models often lack interpretability, which is essential for clinical and biological insights. This paper explores the application of symbolic regression (SR) to microbiome relative abundance data, with a focus on colorectal cancer (CRC). SR, known for its high interpretability, is compared against traditional machine learning models, e.g., random forest, gradient boosting decision trees. These models are evaluated based on performance metrics such as F1 score and accuracy. We utilize 71 studies encompassing, from various cohorts, over 10,000 samples across 749 species features. Our results indicate that SR not only competes reasonably well in terms of predictive performance, but also excels in model interpretability. SR provides explicit mathematical expressions that offer insights into the biological relationships within the microbiome, a crucial advantage for clinical and biological interpretation. Our experiments also show that SR can help understand complex models like XGBoost via knowledge distillation. To aid in reproducibility and further research, we have made the code openly available at https://github.com/swag2198/microbiome-symbolic-regression .