Supplementary material: Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data

Sara A. Yones - Uppsala University, Department of Cell and Molecular Biology, Computational Biology and Bioinformatics. Science for Life Laboratory, SciLifeLab

Alva Annett - Uppsala University, Department of Cell and Molecular Biology. Science for Life Laboratory, SciLifeLab

Patricia Stoll - ETH Zurich, Department of Biosystems Science and Engineering

Klev Diamanti - Uppsala University, Department of Immunology, Genetics and Pathology. Science for Life Laboratory, SciLifeLab

Linda Holmfeldt - Uppsala University, Department of Immunology, Genetics and Pathology, Experimental and Clinical Oncology. Science for Life Laboratory, SciLifeLab

Supplementary tables for manuscript "Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data".

Transcriptomic analyses are commonly used to identify differentially expressed genes between patients and controls, or within individuals across disease courses. These methods, whilst effective, cannot encompass the combinatorial effects of genes driving disease. We applied rule-based machine learning (RBML) models and rule networks (RN) to an existing paediatric Systemic Lupus Erythematosus (SLE) blood expression dataset, with the goal of developing gene networks to separate low and high disease activity (DA1 and DA3). The resultant model had an 81% accuracy to distinguish between DA1 and DA3, with unsupervised hierarchical clustering revealing additional subgroups indicative of the immune axis involved or state of disease flare. These subgroups correlated with clinical variables, suggesting that the gene sets identified may further the understanding of gene networks that act in concert to drive disease progression. This included role