Journal article

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

Grants

Awarded by University of Melbourne


Awarded by Melbourne Bioinformatics


Awarded by Victorian Cancer Agency


Funding Acknowledgements

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).