A cooperative learning framework for the integration of metabolomic data from multiple cohorts and common phenotype identification

a mixed model. The approach was first evaluated in controlled conditions using synthetic data, and then applied to two existing untargeted metabolomics human datasets. Preliminary assessment focused on batch effect reduction across datasets, and agreement between early and late fusion outputs. Using real word data, the results showed that 10% of the initial features were stable across early and late fusion. This showed improved consistency compared to when they were published separately on the integrated dataset. All results demonstrate the ability of the proposed approach to capture the common part of phenotypes. The developed integration model based on cooperative learning leverages the complementary strengths of early and late fusion, offering an efficient solution for metabolomics data integration, enhancing the reliability of potential biomarker discovery.