Metabolite alterations are crucial for understanding diseases, yet large-scale untargeted metabolomics faces challenges in signal detection and dataset integration. Here, the authors introduce mzLearn, a data-driven MS¹ signal-detection method that that corrects instrumental drift and enhances signal accuracy and consistency, enabling the development of pre-trained models for untargeted metabolomics.
- Leila Pirhaji
- Jonah Eaton
- Maria Karasarides
