Development and verification of a combined diagnostic model for primary Sjogren's syndrome by integrated bioinformatics analysis and machine learning.

ABSTRACT: Primary Sjögren's syndrome (pSS) is a chronic, systemic autoimmune disease mostly affecting the exocrine glands. This debilitating condition is complex and specific treatments remain unavailable. There is a need for the development of novel diagnostic models for early screening. Four gene profiling datasets were downloaded from the Gene Expression Omnibus database. The 'limma' software package was used to identify differentially expressed genes (DEGs). A random forest-supervised classification algorithm was used to screen disease-specific genes, and three machine learning algorithms, including artificial neural networks (ANN), random forest (RF), and support vector machines (SVM), were used to build a pSS diagnostic model. The performance of the model was measured using its area under the

SUBMITTER: Yang K 

PROVIDER: S-EPMC10224947 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature