Reducing Quantitative Uncertainty Caused by Data Processing in Untargeted Metabolomics.

Processing liquid chromatography-mass spectrometry-based metabolomics data using computational programs often introduces additional quantitative uncertainty, termed computational variation in a previous work. This work develops a computational solution to automatically recognize metabolic features with computational variation in a metabolomics data set. This tool, AVIR (short for "Accurate eValuation of alIgnment and integRation"), is a support vector machine-based machine learning strategy (https://github.