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

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 Elast

SUBMITTER: Palmer SN 

PROVIDER: S-EPMC12236860 | biostudies-literature | 2025 Jun

REPOSITORIES: biostudies-literature