ArticleBMC biology2026
Network-based machine learning to identify biomarkers for systemic lupus erythematosus.
Article in BMC biology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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11 authors.
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Abstract
backgroundSystemic lupus erythematosus (SLE) is a complex autoimmune disease, making accurate diagnosis and effective treatment challenging. Despite the critical need for reliable biomarkers, conventional differential gene expression (DGE) analyses often yield high false-positive rates and have limited ability to identify clinically actionable targets, leaving a significant gap in SLE precision medicine.
resultsTo address this, we developed NetSLE, a network-based machine learning framework that integrates diverse SLE-related prior knowledge with comprehensive biological networks. By effectively filtering out false positives from differentially expressed genes (DEGs), NetSLE identified a robust panel of 150 key biomarkers. Clinically, the NetSLE-derived biomarkers outperformed conventional markers and full transcriptomes in predicting disease activity across independent cohorts. Furthermore, they successfully identified experimentally validated drug repurposing candidates (e.g., lipid-modifying and antithrombotic agents) and enabled precise stratification of patients into distinct immunological subtypes (AS1 and AS2).
conclusionsNetSLE offers a translatable approach to overcome the limitations of traditional biomarker discovery. The clinically sized 150-gene panel provides a practical tool for enhancing diagnostic precision, guiding targeted treatments, and advancing personalized medicine in SLE.
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