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• W4293252893 abstract "Applying machine learning strategies to interpret mass spectrometry data has the potential to revolutionize the way in which disease is diagnosed, prognosed, and treated. A persistent and tedious obstacle, however, is relaying mass spectrometry data to the machine learning algorithm. Given the native format and large size of mass spectrometry data files, preprocessing is a critical step. To ameliorate this challenge, we sought to create an easy-to-use, continuous pipeline that runs from data acquisition to the machine learning algorithm. Here, we present a start-to-finish pipeline designed to facilitate supervised and unsupervised classification of mass spectrometry data. The input can be any ESI data set collected by LC-MS or flow injection, and the output is a machine learning ready matrix, in which each row is a feature (an abundance of a particular m/z ), and each column is a sample. This workflow provides automated handling of large mass spectrometry data sets for researchers seeking to implement machine learning strategies but who lack expertise in programming/coding to rapidly format the data. We demonstrate how the pipeline can be used on two different mass spectrometry data sets: 1) ESI-MS of fingerprint lipid compositions acquired by direct infusion and, 2) LC-MS of IgG glycopeptides. This workflow is uncomplicated and provides value via its simplicity and effectiveness." @default.
• W4293252893 created "2022-08-27" @default.
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• W4293252893 date "2022-08-23" @default.
• W4293252893 modified "2023-09-26" @default.
• W4293252893 title "Leveraging R (LevR) for fast processing of mass spectrometry data and machine learning: Applications analyzing fingerprints and glycopeptides" @default.
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• W4293252893 doi "https://doi.org/10.3389/frans.2022.961592" @default.
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