ArticleScientific reports2022
Interpretable machine learning identifies paediatric Systemic Lupus Erythematosus subtypes based on gene expression data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
9 citing papers in PubMed, 10 citations in OpenAlex.
- From pathogenesis to precision medicine in systemic lupus erythematosus: Emerging biomarkers and targeted interventions.iScience · 2026Review
- Immune ontogeny in childhood-onset systemic lupus erythematosus: effect of immune maturity on disease phenotype and therapeutic responses.Frontiers in immunology · 2026Review
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Autophagy and machine learning: Unanswered questions.Biochimica et biophysica acta. Molecular basis of disease · 2024Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- What is circulating factor disease and how is it currently explained?Pediatric nephrology (Berlin, Germany) · 2023Review
- An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients.iScience · 2023Article
- Analysis of transcriptomic features reveals molecular endotypes of SLE with clinical implications.Genome medicine · 2023Article
- Application of Machine Learning Models in Systemic Lupus Erythematosus.International journal of molecular sciences · 2023Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 4 countries.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.