Biomarker Selection and a Prospective Metabolite-Based Machine Learning Diagnostic for Lyme Disease

Authors

Eric R. Kehoe, Department of Mathematics, Colorado State University, Fort Collins, CO, 80523, USA. Eric.Kehoe@colostate.edu.
Bryna L. Fitzgerald, Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA.
Barbara Graham, Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA.
M Nurul Islam, Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA.Follow
Kartikay Sharma, Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA.
Gary P. Wormser, Department of Medicine, New York Medical College, Valhalla, NY, 10595, USA.Follow
John T. Belisle, Department of Microbiology, Immunology & Pathology, Colorado State University, Fort Collins, CO, 80523, USA.
Michael J. Kirby, Department of Computer Science, Colorado State University, Fort Collins, CO, 80523, USA.

Author Type(s)

Faculty

DOI

10.1038/s41598-022-05451-0

Journal Title

Scientific Reports

First Page

1478

Document Type

Article

Publication Date

1-27-2022

Department

Medicine

Abstract

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.

Recommended Citation

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. Scientific Reports, 12 (1), 1478. https://doi.org/10.1038/s41598-022-05451-0