Identifying Optimal Machine Learning Approaches for Microbiome-Metabolomics Integration with Stable Feature Selection.

Microbiome research has been limited by methodological inconsistencies. Taxonomy-based profiling presents challenges such as data sparsity, variable taxonomic resolution, and the reliance on DNA-based profiling, which provides limited functional insight. Multi-omics integration has emerged as a promising approach to link microbiome composition with function. However, the lack of standardized methodologies and inconsistencies in machine learning strategies has hindered reproducibility. Additionally, while machine learning can be used to identify key microbial and metabolic features, the stability of feature selection across models and data types remains underexplored, despite its importance for downstream experimental validation and biomarker discovery. Here, we systematically compare Elastic Net, Random Forest, and XGBoost across five multi-omics integration strategies: Concatenation, Averaged Stacking, Weighted Non-negative Least Squares (NNLS), Lasso Stacking, and Partial Least Squares (PLS), as well as individual 'omics models. We evaluate performance across 588 binary and 735 continuous models using microbiome-derived metabolomics and taxonomic data. Additionally, we assess the impact of feature reduction on model performance and feature selection stability. Among the approaches tested, Random Forest combined with NNLS yielded the highest overall performance across diverse datasets. Tree-based methods also demonstrated consistent feature selection across data types and dimensionalities. These results demonstrate how integration strategies, algorithm selection, data dimensionality, and response type impact both predictive performance and the stability of selected features in multi-omics microbiome modeling.