Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease.
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ABSTRACT:
SUBMITTER: Kehoe ER
PROVIDER: S-EPMC8795431 | biostudies-literature | 2022 Jan
REPOSITORIES: biostudies-literature
Publications
Biomarker selection and a prospective metabolite-based machine learning diagnostic for lyme disease.
Kehoe Eric R ER Fitzgerald Bryna L BL Graham Barbara B Islam M Nurul MN Sharma Kartikay K Wormser Gary P GP Belisle John T JT Kirby Michael J MJ
Scientific reports 20220127 1
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 ...[more]
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