Machine learning applied to whole-blood RNA-sequencing data uncovers distinct subsets of patients with systemic lupus erythematosus
William A Figgett, Katherine Monaghan, Milica Ng, Monther Alhamdoosh, Eugene Maraskovsky, Nicholas J Wilson, Alberta Y Hoi, Eric F Morand, Fabienne Mackay
Clinical & Translational Immunology | WILEY | Published : 2019
Awarded by University of Melbourne
Awarded by Melbourne Bioinformatics
Awarded by Victorian Cancer Agency
Computational work was performed using the high-performance computing (HPC) resources of the University of Melbourne (Project#punim0259) and Melbourne Bioinformatics (Project#UOM0044). We acknowledge the HPC training and technical assistance provided by the University of Melbourne, Melbourne Bioinformatics, and the Australian National Computational Infrastructure. This research was supported by use of the NeCTAR Research Cloud, a collaborative Australian research platform supported by the National Collaborative Research Infrastructure Strategy. We acknowledge Dr Kim-Anh Le Cao for helpful discussions about multivariate statistics methods in the mixOmics R package. WF was supported by funding from the Victorian Cancer Agency (grant#ECSG15029).