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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