ArticleArthritis research & therapy2017
Biological function integrated prediction of severe radiographic progression in rheumatoid arthritis: a nested case control study.
Article in Arthritis research & therapy, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Development of a Machine Learning Model to Predict the Use of Surgery in Patients With Rheumatoid Arthritis.Arthritis care & research · 2024Article
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus.BioData mining · 2021Article
- Molecular and Cellular Heterogeneity in Rheumatoid Arthritis: Mechanisms and Clinical Implications.Frontiers in immunology · 2021Review
- Personalized prediction of disease activity in patients with rheumatoid arthritis using an adaptive deep neural network.PloS one · 2021Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- 'RA and the microbiome: do host genetic factors provide the link?Journal of autoimmunity · 2019Review
- Genetic-Driven Druggable Target Identification and Validation.Trends in genetics : TIG · 2018Review
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Authors and funding
10 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundRadiographic progression is reported to be highly heritable in rheumatoid arthritis (RA). However, previous study using genetic loci showed an insufficient accuracy of prediction for radiographic progression. The aim of this study is to identify a biologically relevant prediction model of radiographic progression in patients with RA using a genome-wide association study (GWAS) combined with bioinformatics analysis.
methodsWe obtained genome-wide single nucleotide polymorphism (SNP) data for 374 Korean patients with RA using Illumina HumanOmni2.5Exome-8 arrays. Radiographic progression was measured using the yearly Sharp/van der Heijde modified score rate, and categorized in no or severe progression. Significant SNPs for severe radiographic progression from GWAS were mapped on the functional genes and reprioritized by post-GWAS analysis. For robust prediction of radiographic progression, tenfold cross-validation using a support vector machine (SVM) classifier was conducted. Accuracy was used for selection of optimal SNPs set in the Hanyang Bae RA cohort. The performance of our final model was compared with that of other models based on GWAS results and SPOT (one of the post-GWAS analyses) using receiver operating characteristic (ROC) curves. The reliability of our model was confirmed using GWAS data of Caucasian patients with RA.
resultsA total of 36,091 significant SNPs with a p value <0.05 from GWAS were reprioritized using post-GWAS analysis and approximately 2700 were identified as SNPs related to RA biological features. The best average accuracy of ten groups was 0.6015 with 85 SNPs, and this increased to 0.7481 when combined with clinical information. In comparisons of the performance of the model, the 0.7872 area under the curve (AUC) in our model was superior to that obtained with GWAS (AUC 0.6586, p value 8.97 × 10
conclusionsUsing various biological functions of SNPs and repeated machine learning, our model could predict severe radiographic progression relevantly and robustly in patients with RA compared with models using only GWAS results or other post-GWAS tools.
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