Blood transcriptomics to facilitate diagnosis and stratification in pediatric rheumatic diseases - a proof of concept study.

ABSTRACT:

Background

Transcriptome profiling of blood cells is an efficient tool to study the gene expression signatures of rheumatic diseases. This study aims to improve the early diagnosis of pediatric rheumatic diseases by investigating patients' blood gene expression and applying machine learning on the transcriptome data to develop predictive models.

Methods

RNA sequencing was performed on whole blood collected from children with rheumatic diseases. Random Forest classification models were developed based on the transcriptome data of 48 rheumatic patients, 46 children with viral infection, and 35 controls to classify different disease groups. The performance of these classifiers was evaluated by leave-one-out cross-validation. Analyses of differentially expressed genes (DEG), gen

SUBMITTER: Ha MK 

PROVIDER: S-EPMC9575227 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature