Letter to the editor: testing the effectiveness of MyPROSLE in classifying patients with lupus nephritis

Brief Bioinform. 2023 Sep 20;24(5):bbad322. doi: 10.1093/bib/bbad322.

Abstract

Because of the clinical heterogeneity among patients with systemic lupus erythematosus (SLE), developing molecular profiles that predict clinical features can be useful in creating a personalized approach to treatment. Toro-Domínguez et al. created a web tool to aid in therapeutic decision making for clinicians that predicts clinical features associated with SLE from blood transcriptomic data. Specifically, they present a machine learning model that predicts the presence of proliferative nephritis from blood transcriptomics. Here, we report use of the tool in independent datasets and found that it did not perform sufficiently well to consider replacement of the standard kidney biopsy as a diagnostic procedure.

Keywords: lupus nephritis; machine learning; precision medicine; systemic lupus erythematosus.

Publication types

  • Letter
  • Research Support, Non-U.S. Gov't
  • Comment

MeSH terms

  • Gene Expression Profiling
  • Humans
  • Lupus Erythematosus, Systemic*
  • Lupus Nephritis* / diagnosis
  • Lupus Nephritis* / genetics
  • Machine Learning
  • Transcriptome