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We utilize tools of machine learning (ML), e.g., sparse support vector machines (SSVM), iterative feature removal (IFR), and\n                    <jats:italic>k<\/jats:italic>\n                    -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.\n                  <\/jats:p>","DOI":"10.1038\/s41598-022-05451-0","type":"journal-article","created":{"date-parts":[[2022,1,27]],"date-time":"2022-01-27T06:03:15Z","timestamp":1643263395000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease"],"prefix":"10.1038","volume":"12","author":[{"given":"Eric R.","family":"Kehoe","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bryna L.","family":"Fitzgerald","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Barbara","family":"Graham","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"M. 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He has been an expert witness in malpractice cases involving Lyme disease; and is an unpaid board member of the American Lyme Disease Foundation. Drs. Belisle and Wormser are Inventors on the U.S. Patent 10669567 B2 High Sensitivity Method for Early Lyme Disease Detection. All other authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"1478"}}