ArticleiScience2023
An interpretable machine learning pipeline based on transcriptomics predicts phenotypes of lupus patients.
Article in iScience, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
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Who cites it
6 citing papers in PubMed, 2 citations in OpenAlex.
- Article
- A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.Health science reports · 2026Article
- Review
- Explainable machine learning-based identification of transcriptomic biomarkers in CD1c+ dendritic cells for non-infectious uveitis: an integrative analysis of bulk RNA-seq data.Frontiers in bioinformatics · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Machine learning (ML) has the potential to identify subsets of patients with distinct phenotypes from gene expression data. However, phenotype prediction using ML has often relied on identifying important genes without a systems biology context. To address this, we created an interpretable ML approach based on blood transcriptomics to predict phenotype in systemic lupus erythematosus (SLE), a heterogeneous autoimmune disease. We employed a sequential grouped feature importance algorithm to assess the performance of gene sets, including immune and metabolic pathways and cell types, known to be abnormal in SLE in predicting disease activity and organ involvement. Gene sets related to interferon, tumor necrosis factor, the mitoribosome, and T cell activation were the best predictors of phenotype with excellent performance. These results suggest potential relationships between the molecular pathways identified in each model and manifestations of SLE. This ML approach to phenotype prediction can be applied to other diseases and tissues.
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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.