Accurate Machine Learning Model to Diagnose Chronic Autoimmune Diseases Utilizing Information From B Cells and Monocytes.

ABSTRACT: Heterogeneity and limited comprehension of chronic autoimmune disease pathophysiology cause accurate diagnosis a challenging process. With the increasing resources of single-cell sequencing data, a reasonable way could be found to address this issue. In our study, with the use of large-scale public single-cell RNA sequencing (scRNA-seq) data, analysis of dataset integration (3.1 × 10⁵ PBMCs from fifteen SLE patients and eight healthy donors) and cellular cross talking (3.8 × 10⁵ PBMCs from twenty-eight SLE patients and eight healthy donors) were performed to identify the most crucial information characterizing SLE. Our findings revealed that the interactions among the PBMC subpopulations of SLE patients may be weakened under the inflammatory microenvironment, which co

SUBMITTER: Ma Y 

PROVIDER: S-EPMC9065417 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature