Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data.

ABSTRACT: 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, suggest

SUBMITTER: Yones SA 

PROVIDER: S-EPMC9076598 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature