Supplementary material: Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data Supplementary material: Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data 2024-340-1 https://doi.org/10.57804/wnev-6d20 Swedish National Data Service Svensk nationell datatjänst Landing page Supplementary material: Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data Supplementary material: Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data 2024-340-1 https://doi.org/10.57804/wnev-6d20 Yones, Sara A. Yones, Sara A. Annett, Alva Annett, Alva Stoll, Patricia Stoll, Patricia Diamanti, Klev Diamanti, Klev Holmfeldt, Linda Holmfeldt, Linda Barrenäs, Fredrik Barrenäs, Fredrik Meadows, Jennifer Meadows, Jennifer Komorowski, Jan Komorowski, Jan Swedish National Data Service Svensk nationell datatjänst Landing page bioinformatics bioinformatik rheumatology reumatologi 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 roles for genes i) induced by interferons (IFI35 and OTOF), ii) key to SLE cell types (KLRB1 encoding CD161), or iii) with roles in autophagy and NF-κB pathway responses (CKAP4). As demonstrated here, RBML approaches have the potential to reveal novel gene patterns from within a heterogeneous disease, facilitating patient clinical and therapeutic stratification. The dataset was originally published in DiVA and moved to SND in 2024. 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 roles for genes i) induced by interferons (IFI35 and OTOF), ii) key to SLE cell types (KLRB1 encoding CD161), or iii) with roles in autophagy and NF-κB pathway responses (CKAP4). As demonstrated here, RBML approaches have the potential to reveal novel gene patterns from within a heterogeneous disease, facilitating patient clinical and therapeutic stratification. Datasetet har ursprungligen publicerats i DiVA och flyttades över till SND 2024. Access to data through SND. Data are freely accessible. Åtkomst till data via SND. Data är fritt tillgängliga. openAccess