ArticleBioinformatics advances2026
SLE-diseaseome: a comprehensive meta-collection of systemic lupus erythematosus relevant functional pathways.
Article in Bioinformatics advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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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
1 citing paper in PubMed.
- Benchmarking single-sample gene set scoring methods for application in precision medicine.Briefings in bioinformatics · 2025Article
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Authors and funding
7 authors.
Funding
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Abstract
Motivation: Systemic lupus erythematosus patients exhibit a broad clinical spectrum of manifestations and suffer from high rates of treatment failure. These can be attributed to disease heterogeneity due to differentially dysregulated pathways. Precision medicine considering the individualized molecular disease driving mechanisms is a promising strategy to address challenges imposed by disease heterogeneity. Available patient blood transcriptome data coupled with pathway-based single-sample scoring approaches have been extensively employed to reveal molecular footprints of disease states and progression as well as delineate population heterogeneity. However, systemic understanding of pathways involved in disease pathogenesis remains lacking. Results: We created a SLE-diseaseome, an integrative multi-cohort collection of disease-relevant functional gene sets. This resource contains a comprehensive collection of disease-specific gene signatures combining knowledge from several pathway databases and signature sources robustly defined by integrating multiple studies. It offers reliable and extensive reference signatures in a disease-specific manner for functional interpretation of molecular data from clinical studies. Availability and implementation: The code used to run the pipeline and the R object containing the SLE-diseaseome collection are available at https://github.com/dtordom/SLEDiseaseome.
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Registered trials
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