Biomarker Selection and a Prospective Metabolite-Based Machine Learning Diagnostic for Lyme Disease
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.
Kehoe, E. R., Fitzgerald, B. L., Graham, B., Islam, M. N., Sharma, K., Wormser, G. P., Belisle, J. T., & Kirby, M. J. (2022). Biomarker Selection and a Prospective Metabolite-Based Machine Learning Diagnostic for Lyme Disease. https://doi.org/10.1038/s41598-022-05451-0