REVIVE MED

Artificial Intelligence + Metabolomics

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.

Metabolite annotation within a multi-omic context

Inferring Metabolite Identity: Accurately identifies metabolite signals using propriety knowledge-based graph.

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.