ArticleScientific reports2019
Machine learning approaches to predict lupus disease activity from gene expression data.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers, 1 of them a synthesis that pooled it.
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Who cites it
41 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for Diagnosis of Systemic Lupus Erythematosus: A Systematic Review and Meta-Analysis.Computational intelligence and neuroscience · 2022Pooled it
- Unveiling Endotypes in Systemic Lupus Erythematosus Through Multiomic Analysis: Insights Into Cardiovascular and Renal Complications.Arthritis & rheumatology (Hoboken, N.J.) · 2026Article
- Machine learning reveals X chromosome transcriptional signatures that classify systemic lupus erythematosus in females.BMC rheumatology · 2026Article
- A Narrative Review on Integrative Bioinformatics Approaches for microRNA Research in Familial Mediterranean Fever: Current Insights and Future Directions.Health science reports · 2026Article
- Genes causative of primary immunodeficiency are risk factors for and are over-expressed in systemic lupus erythematosus.Frontiers in immunology · 2026Article
- Precision and contextualization in claims-based disease activity assessment for systemic lupus erythematosus in Korean populations.Journal of rheumatic diseases · 2026Article
- Applications of artificial intelligence in systemic lupus erythematosus: integrating multi-omics data for precision medicine.Frontiers in immunology · 2026Review
- Proteolytic profiling of human plasma reveals an immunoactive complement C3 fragment.The EMBO journal · 2025Article
- Generative prediction of causal gene sets responsible for complex traits.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Application of machine learning in assessing disease activity in SLE.Lupus science & medicine · 2025Article
- Screening and validating genes associated with cuproptosis in systemic lupus erythematosus by expression profiling combined with machine learning.Biomolecules & biomedicine · 2025Article
- A serum biomarker panel and miniarray detection system for tracking disease activity and flare risk in lupus nephritis.Frontiers in immunology · 2025Article
- How artificial intelligence can enable personalized mesenchymal stem cell-based therapeutic strategies in systemic lupus erythematosus.Frontiers in immunology · 2025Review
- Multimodal AI/ML for discovering novel biomarkers and predicting disease using multi-omics profiles of patients with cardiovascular diseases.Scientific reports · 2024Article
- Emerging Strategies in Drug Development and Clinical Care in the Era of Personalized and Precision Medicine.Pharmaceutics · 2024Article
- Systemic lupus in the era of machine learning medicine.Lupus science & medicine · 2024Review
- Novel multiclass classification machine learning approach for the early-stage classification of systemic autoimmune rheumatic diseases.Lupus science & medicine · 2024Article
- Discovering biomarkers associated and predicting cardiovascular disease with high accuracy using a novel nexus of machine learning techniques for precision medicine.Scientific reports · 2024Article
- IntelliGenes: a novel machine learning pipeline for biomarker discovery and predictive analysis using multi-genomic profiles.Bioinformatics (Oxford, England) · 2023Article
- Cancer Diagnosis through Contour Visualization of Gene Expression Leveraging Deep Learning Techniques.Diagnostics (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The integration of gene expression data to predict systemic lupus erythematosus (SLE) disease activity is a significant challenge because of the high degree of heterogeneity among patients and study cohorts, especially those collected on different microarray platforms. Here we deployed machine learning approaches to integrate gene expression data from three SLE data sets and used it to classify patients as having active or inactive disease as characterized by standard clinical composite outcome measures. Both raw whole blood gene expression data and informative gene modules generated by Weighted Gene Co-expression Network Analysis from purified leukocyte populations were employed with various classification algorithms. Classifiers were evaluated by 10-fold cross-validation across three combined data sets or by training and testing in independent data sets, the latter of which amplified the effects of technical variation. A random forest classifier achieved a peak classification accuracy of 83 percent under 10-fold cross-validation, but its performance could be severely affected by technical variation among data sets. The use of gene modules rather than raw gene expression was more robust, achieving classification accuracies of approximately 70 percent regardless of how the training and testing sets were formed. Fine-tuning the algorithms and parameter sets may generate sufficient accuracy to be informative as a standalone estimate of disease activity.
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