Trends in Machine Learning and Feature Selection Stability for Human Gut Microbiome (Shotgun Metagenomics) and Metabolomics Matched Datasets
Year: 2025
Type: preprint
Abstract: 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 human gut microbiome-derived metabolomics and taxonomic data derived from metagenomics shotgun sequencing 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.
Language: English
Topic: Gut microbiota and health
Subfield: Molecular Biology
Domain: Life Sciences
Open Access status: green
Awards: Understanding the molecular mechanisms regulating fungal colonization and disease in the mammalian intestinal niche, Tools for integrative genomics and disease association study for the X chromosome, Methods to Identify, Validate & Interpret GWAS Loci in Multi-ethnic Meta-analysis, Antibiotic Resistance Determination Utilizing Machine Learning, Integrative Immunology Training Grant, Cancer Center Support Grant, Methods to unveil sex-specific genetic architecture in trans-ancestry meta-analysis
OpenAlex ID: W4411729364