Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data
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, 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.
Holmfeldt Linda、Komorowski Jan、Annett Alva、Diamanti Klev、Yones Sara A.、Barren?s Carl Fredrik、Stoll Patricia、Meadows Jennifer R. S.
Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala UniversityScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala University||Washington National Primate Research Center||Swedish Collegium for Advanced Study||The Institute of Computer Science, Polish Academy of SciencesScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala UniversityDepartment of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala UniversityScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala UniversityScience for Life Laboratory, Department of Cell and Molecular Biology, Uppsala UniversityDepartment of Biosystems Science and Engineering, ETH ZurichScience for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University
医学研究方法基础医学内科学
Holmfeldt Linda,Komorowski Jan,Annett Alva,Diamanti Klev,Yones Sara A.,Barren?s Carl Fredrik,Stoll Patricia,Meadows Jennifer R. S..Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data[EB/OL].(2025-03-28)[2025-04-26].https://www.biorxiv.org/content/10.1101/2021.06.03.446884.点此复制
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