A Biologically Informed Machine Learning Pipeline Uncovers Metabolic Features of Intestinal Barrier Dysfunction

Intestinal barrier dysfunction is a pivotal factor in diverse pathologies, yet clear diagnostic criteria remain elusive, and biomarker discovery is often hindered by the high costs of large-scale clinical cohorts. Here, we developed a biologically informed machine learning (ML) pipeline that integrates function-guided network biology with ensemble feature selection (EFS) to identify key metabolic features associated with this condition. As an integrative framework, the pipeline combines knowledge-based screening and computational prioritization with biological validation, reconciling statistical rigor with biological interpretability. We first constructed a heterogeneous data set from multiphenotypic murine models and derived a continuous intestinal barrier index as a predicted variable. This approach reframes the conventional classification paradigm into a supervised regression task, enabling the discovery of disease-relevant clues without clear diagnostic end points. By leveraging network biology-informed EFS, we identified a functional feature subset of 10 core metabolites. The subset demonstrated remarkable stability across five distinct regression architectures, maintaining consistent robust performance (R²: 0.604-0.654; mean absolute error: 0.319-0.352). Furthermore, independent clinical validation coupled with in vitro biotransformation assays confirmed the applicability of these prioritized metabolic features, highlighting serotonin, N-acetylputrescine, d-phenylalanine, and hippuric acid as functional metabolic features of barrier dysfunction. In this work, ML was repurposed as a tool for feature prioritization, robustness evaluation, and hypothesis generation, reducing reliance on large cohorts. This integrated pipeline facilitates the identification of intestinal barrier dysfunction associated metabolic features and enables high-efficiency evaluation of multicomponent therapeutic responses, offering new insights into the management of complex multifactorial diseases.