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

Sara A. Yones - Uppsala universitet, Institutionen för cell- och molekylärbiologi, Beräkningsbiologi och bioinformatik. Science for Life Laboratory, SciLifeLab

Alva Annett - Uppsala universitet, Biologiska sektionen, Institutionen för cell- och molekylärbiologi. Science for Life Laboratory, SciLifeLab

Patricia Stoll - ETH Zurich, Institutionen för biosystemvetenskap och teknik

Klev Diamanti - Uppsala universitet, Institutionen för immunologi, genetik och patologi. Science for Life Laboratory, SciLifeLab

Linda Holmfeldt - Uppsala universitet, Institutionen för immunologi, genetik och patologi, Experimentell och klinisk onkologi. 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