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

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.《基于基因表达数据的可解释机器学习识别儿童系统性红斑狼疮亚型》论文补充附表。转录组分析（Transcriptomic analyses）通常用于识别患者与健康对照之间，或个体在不同疾病进程中的差异表达基因。此类方法虽具备有效性，但无法涵盖驱动疾病的基因组合效应。我们将基于规则的机器学习（rule-based machine learning, RBML）模型与规则网络（rule networks, RN）应用于已有的儿童系统性红斑狼疮（Systemic Lupus Erythematosus, SLE）血液表达数据集，旨在构建基因网络以区分疾病低活动度与高活动度（DA1与DA3）。所得模型区分DA1与DA3的准确率达81%，无监督层次聚类揭示了额外亚组，这些亚组可提示所涉及的免疫轴或疾病发作状态。这些亚组与临床变量存在关联，提示所鉴定的基因集可加深对协同驱动疾病进展的基因网络的理解。其中涉及的基因包括：i) 干扰素诱导基因（IFI35与OTOF）；ii) SLE细胞类型相关关键基因（编码CD161的KLRB1）；iii) 参与自噬与NF-κB通路应答的基因（CKAP4）。正如本研究所示，基于规则的机器学习方法有望从异质性疾病中揭示新型基因模式，助力患者的临床与治疗分层。该数据集最初发表于DiVA数据库，并于2024年迁移至SND数据库。