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Kehoe, Eric R; Fitzgerald, Bryna L; Graham, Barbara; Islam, M Nurul; Sharma, Kartikay; Wormser, Gary P; Belisle, John T; Kirby, Michael J
Scientific reports, 01/2022, Letnik: 12, Številka: 1Journal Article
We provide a pipeline for data preprocessing, biomarker selection, and classification of liquid chromatography-mass spectrometry (LCMS) serum samples to generate a prospective diagnostic test for Lyme disease. We utilize tools of machine learning (ML), e.g., sparse support vector machines (SSVM), iterative feature removal (IFR), and k-fold feature ranking to select several biomarkers and build a discriminant model for Lyme disease. We report a 98.13% test balanced success rate (BSR) of our model based on a sequestered test set of LCMS serum samples. The methodology employed is general and can be readily adapted to other LCMS, or metabolomics, data sets.
