CASE STUDY

Polly PeakML Speeds Up Insight Derivation from Untargeted Metabolomics

Key Highlights

  • The company aimed to facilitate an initiative for ML-model based classification of untargeted metabolomics data. This approach could then be utilized for investigational therapies in preclinical development.
  • Peak detection in metabolomics studies demands expert evaluation of global profile of metabolites and is a tedious, error-prone process.
  • To help the company gain better insights from the untargeted metabolomics data, Polly PeakML pipeline was used to curate and analyze the data.
  • The company was able to identify correlation between metabolites;  pathways associated with the diseased and rescue conditions and gain many more insights using Polly PeakML.
  • Lastly and importantly, the company was able to perform the task in just a few minutes and the entire analysis in a few weeks!
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