ArticleEBioMedicine2022
Functional coding haplotypes and machine-learning feature elimination identifies predictors of Methotrexate Response in Rheumatoid Arthritis patients.
Article in EBioMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed, 22 citations in OpenAlex.
- Predicting VNN resistance in European sea bass using machine learning on high dimensional low sample size data.Frontiers in bioinformatics · 2026Article
- Assessing individual genetic susceptibility to metabolic syndrome: interpretable machine learning method.Annals of medicine · 2025Article
- Blood-based DNA methylation markers for autism spectrum disorder identification using machine learning.Epigenomics · 2025Article
- Common genetic variants do not impact clinical prediction of methotrexate treatment outcomes in early rheumatoid arthritis.Journal of internal medicine · 2025Article
- Artificial intelligence to predict treatment response in rheumatoid arthritis and spondyloarthritis: a scoping review.Rheumatology international · 2025Article
- Factors Underlying Failure of Methotrexate Treatment in Rheumatoid Arthritis: Implications in Personalized Care.Archives of internal medicine research · 2025Article
- Development and evaluation of a multivariable prediction model for clinical improvement in an established cohort of Colombian rheumatoid arthritis patients.Therapeutic advances in musculoskeletal disease · 2025Article
- Prediction of High-Dose Methotrexate Blood Concentration in Osteosarcoma Patients Using Machine Learning.Drug design, development and therapy · 2025Article
- Towards Personalized Medicine in Rheumatoid Arthritis.Open access rheumatology : research and reviews · 2024Review
- Advancing precision rheumatology: applications of machine learning for rheumatoid arthritis management.Frontiers in immunology · 2024Review
- A survey of artificial intelligence in rheumatoid arthritis.Rheumatology and immunology research · 2023Article
- SNP- and haplotype-based single-step genomic predictions for body weight, wool, and reproductive traits in North American Rambouillet sheep.Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie · 2023Article
- Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.Journal of translational medicine · 2023Article
- Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification.Biomedicines · 2022Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Development and Evaluation of a Machine Learning Prediction Model for Small-for-Gestational-Age Births in Women Exposed to Radiation before Pregnancy.Journal of personalized medicine · 2022Article
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMajor challenges in large scale genetic association studies include not only the identification of causative single nucleotide polymorphisms (SNPs), but also accounting for SNP-SNP interactions. This study thus proposes a novel feature engineering approach integrating potentially functional coding haplotypes (pfcHap) with machine-learning (ML) feature selection to identify biologically meaningful, possibly causative genetic factors, that take into consideration potential SNP-SNP interactions within the pfcHap, to best predict for methotrexate (MTX) response in rheumatoid arthritis (RA) patients.
methodsExome sequencing from 349 RA patients were analysed, of which they were split into training and unseen test set. Inferred pfcHaps were combined with 30 non-genetic features to undergo ML recursive feature elimination with cross-validation using the training set. Predictive capacity and robustness of the selected features were assessed using six popular machine learning models through a train set cross-validation and evaluated in an unseen test set.
findingsSignificantly, 100 features (95 pfcHaps, 5 non-genetic factors) were identified to have good predictive performance (AUC: 0.776-0.828; Sensitivity: 0.656-0.813; Specificity: 0.684-0.868) across all six ML models in an unseen test dataset for the prediction of MTX response in RA patients.
interpretationMajority of the predictive pfcHap SNPs were predicted to be potentially functional and some of the genes in which the pfcHap resides in were identified to be associated with previously reported MTX/RA pathways.
fundingSingapore Ministry of Health's National Medical Research Council (NMRC) [NMRC/CBRG/0095/2015; CG12Aug17; CGAug16M012; NMRC/CG/017/2013]; National Cancer Center Research Fund and block funding Duke-NUS Medical School.; Singapore Ministry of Education Academic Research Fund Tier 2 grant MOE2019-T2-1-138.
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