Our patented AI-based platform overcomes critical challenges in large-scale metabolomics, such as signal detection and annotation within a multi-omics context.
First-in-Class Generative Models for Metabolomics
Powered by mzLearn, a data-driven LC/MS signal detection algorithm (bioRxiv link)
Parameter-Free, Scalable Detection
mzLearn identifies the highest-quality metabolite signals without requiring any input parameters or prior knowledge.
Instrument Drift Correction
By generating synthetic quality-control data, mzLearn effectively corrects retention time and intensity drifts caused by extended run order or batch effects.
Pre-trained Generative Models
First-in-class pre-trained generative models for large-scale metabolomics captured metabolite representations associated with demographic and clinical variables.
Enhanced Clinical Predictions
Fine-tuning pre-trained models significantly improves performance in downstream clinical tasks, driving more accurate and actionable insights.
Multi-Omic Integration: Integrating metabolite data with genomic, transcriptomic, and proteomic layers to discover a comprehensive understanding of biological systems.
Proven Innovation: developed at the Fraenkel lab at MIT Biological Engineering Department, and published in Nature Methods.