Machine learning based on metabolomics reveals potential targets and biomarkers for primary Sjogren's syndrome.

ABSTRACT: Background: Using machine learning based on metabolomics, this study aimed to construct an effective primary Sjogren's syndrome (pSS) diagnostics model and reveal the potential targets and biomarkers of pSS. Methods: From a total of 39 patients with pSS and 38 healthy controls (HCs), serum specimens were collected. The samples were analyzed by ultra-high-performance liquid chromatography coupled with high-resolution mass spectrometry. Three machine learning algorithms, including the least absolute shrinkage and selection operator (LASSO), random forest (RF), and extreme gradient boosting (XGBoost), were used to build the pSS diagnosis models. Afterward, four machine learning methods were used to reduce the dimensionality of the metabolomics data. Finally, metabolites with sig

SUBMITTER: Wang K 

PROVIDER: S-EPMC9483105 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature