ArticleJournal of translational medicine2023
Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.
Article in Journal of translational medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 2 of them syntheses that pooled it.
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Who cites it
17 citing papers in PubMed, 2 syntheses or guidelines pooled it, 25 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Genetic relationship between rheumatoid arthritis and cardiovascular diseases : A systematic review of Mendelian randomization studies.Wiener klinische Wochenschrift · 2025Pooled it
- From genes to environment: A life-course approach to prevent pediatric autoimmune diseases.World journal of clinical pediatrics · 2026Review
- Identification of Key Osteoarthritis-Associated Genes Based on DNA Methylation.International journal of molecular sciences · 2026Article
- Physical activity, genetic predisposition and the risk of rheumatoid arthritis: a prospective cohort study.Frontiers in sports and active living · 2026Article
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Emerging Artificial Intelligence Innovations in Rheumatoid Arthritis and Challenges to Clinical Adoption.Current rheumatology reports · 2025Review
- Diagnostic testing in the genetically complex age-related macular degeneration.Medizinische Genetik : Mitteilungsblatt des Berufsverbandes Medizinische Genetik e.V · 2025Article
- Rheumatoid arthritis patients' perspective on the use of prediction models in clinical decision-making.Rheumatology (Oxford, England) · 2025Article
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- Considerations for the application of polygenic scores to clinical care of individuals with substance use disorders.The Journal of clinical investigation · 2024Review
- The rs3918188 and rs1799983 loci of eNOS gene are associated with susceptibility in patients with systemic lupus erythematosus in Northeast China.Scientific reports · 2024Article
- Predictive genetic panel for adult asthma using machine learning methods.The journal of allergy and clinical immunology. Global · 2024Article
- Genetically transitional disease: conceptual understanding and applicability to rheumatic disease.Nature reviews. Rheumatology · 2024Review
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- Clinical prediction models of rheumatoid arthritis and its complications: focus on cardiovascular disease and interstitial lung disease.Arthritis research & therapy · 2023Review
- A survey of artificial intelligence in rheumatoid arthritis.Rheumatology and immunology research · 2023Article
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe popular statistics-based Genome-wide association studies (GWAS) have provided deep insights into the field of complex disorder genetics. However, its clinical applicability to predict disease/trait outcomes remains unclear as statistical models are not designed to make predictions. This study employs statistics-free machine-learning (ML)-optimized polygenic risk score (PRS) to complement existing GWAS and bring the prediction of disease/trait outcomes closer to clinical application. Rheumatoid Arthritis (RA) was selected as a model disease to demonstrate the robustness of ML in disease prediction as RA is a prevalent chronic inflammatory joint disease with high mortality rates, affecting adults at the economic prime. Early identification of at-risk individuals may facilitate measures to mitigate the effects of the disease.
methodsThis study employs a robust ML feature selection algorithm to identify single nucleotide polymorphisms (SNPs) that can predict RA from a set of training data comprising RA patients and population control samples. Thereafter, selected SNPs were evaluated for their predictive performances across 3 independent, unseen test datasets. The selected SNPs were subsequently used to generate PRS which was also evaluated for its predictive capacity as a sole feature.
resultsThrough robust ML feature selection, 9 SNPs were found to be the minimum number of features for excellent predictive performance (AUC > 0.9) in 3 independent, unseen test datasets. PRS based on these 9 SNPs was significantly associated with (P < 1 × 10
conclusionsThese findings highlight the promise of this ML strategy to identify useful genetic features that can robustly predict disease and amenable to translation for clinical application.
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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.