Assessing variable importance stability using resampling strategies to enhance model interpretability and reliability in metabolomics

Because it is computationally efficient and does not require assumptions about data distribution, the proposed method constitutes a straightforward, generic and relevant approach that is well suited to the needs of a wide range of applications. The broad adoption of this type of methodology will undoubtedly help to achieve more consistent and reproducible results, ultimately advancing the understanding of metabolic patterns.