The feasibility of developing biomarkers from peripheral blood mononuclear cell RNAseq data in children with juvenile idiopathic arthritis using machine learning approaches.

Journal: Arthritis research & therapy
PMID: 31706344

Abstract

BACKGROUND: The response to treatment for juvenile idiopathic arthritis (JIA) can be staged using clinical features. However, objective laboratory biomarkers of remission are still lacking. In this study, we used machine learning to predict JIA activity from transcriptomes from peripheral blood mononuclear cells (PBMCs). We included samples from children with Native American ancestry to determine whether the model maintained validity in an ethnically heterogeneous population.

Authors

  • Kerry E Poppenberg
    Canon Stroke and Vascular Research Center, University at Buffalo Jacobs School of Medicine & Biomedical Sciences, State University of New York, Buffalo, NY, USA.
  • Kaiyu Jiang
    Department of Pediatrics, University at Buffalo, Buffalo, NY, USA.
  • Lu Li
    State Key Laboratory of Freshwater Ecology and Biotechnology, Institute of Hydrobiology, Chinese Academy of Sciences, Wuhan, Hubei, China.
  • Yijun Sun
    Genetics, Genomics, and Bioinformatics Graduate Program, University at Buffalo, Buffalo, NY, USA.
  • Hui Meng
    Department of Urology Surgery, Qilu Hospital of Shandong University Jinan, P. R. China.
  • Carol A Wallace
    Department of Pediatrics, University of Washington, Seattle, WA, USA.
  • Teresa Hennon
    Department of Pediatrics, University at Buffalo, Buffalo, NY, USA.
  • James N Jarvis
    Department of Pediatrics, University at Buffalo, Buffalo, NY, USA. jamesjar@buffalo.edu.

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