Journal article

Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus

William Figgett, Katherine Monaghan, Milica Ng, Monther Alhamdoosh, Eugene Maraskovsky, Nicholas Wilson, Alberta Hoi, Eric Morand, Fabienne Mackay

Published : 2019


ABSTRACT Objective Systemic lupus erythematosus (SLE) is a heterogeneous autoimmune disease that is difficult to treat. There is currently no optimal stratification of patients with SLE, and thus responses to available treatments are unpredictable. Here, we developed a new stratification scheme for patients with SLE, based on the whole-blood transcriptomes of patients with SLE. Methods We applied machine learning approaches to RNA-sequencing (RNA-seq) datasets to stratify patients with SLE into four distinct clusters based on their gene expression profiles. A meta-analysis on two recently published whole-blood RNA-seq datasets was carried out and an additional similar dataset of 30 patients ..

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University of Melbourne Researchers


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