mzLearn: A Data-driven Metabolite Signal Detection Algorithm, bioRxiv


  • Zero-parameter design: No prior knowledge or QC samples are required.
  • Scalability: Capable of handling large-scale datasets.
  • First-in-class Generative Models: Learn metabolite representations linked to demographic and clinical variables, improving the accuracy of downstream predictions.


Multi-omic Network-based Integration, Nature Methods


  • Multi-omic Data Processing: Analysis of transcriptomics, proteomics, and metabolomics data in a unified framework.
  • Proprietary Knowledge-Based Graph: Leverages a curated database of weighted protein-protein interactions and protein-metabolite interactions to contextualize findings.
  • Network Inference: Builds functional interaction networks that map multi-omic drivers of disease or drug response.

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