ArticleBMC proceedings2009
Detecting single-nucleotide polymorphism by single-nucleotide polymorphism interactions in rheumatoid arthritis using a two-step approach with machine learning and a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model.
Article in BMC proceedings, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.Frontiers in immunology · 2025Review
- Exploration of the association between the single-nucleotide polymorphism of co-stimulatory system and rheumatoid arthritis.Frontiers in immunology · 2023Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- A combined risk model for the multi-encompassing identification of heterogeneities of prognoses, biological pathway variations and immune states for sepsis patients.BMC anesthesiology · 2022Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Article
- Three-Month Real-Time Dengue Forecast Models: An Early Warning System for Outbreak Alerts and Policy Decision Support in Singapore.Environmental health perspectives · 2016Article
- Next generation modeling in GWAS: comparing different genetic architectures.Human genetics · 2014Article
- Fast genomic predictions via Bayesian G-BLUP and multilocus models of threshold traits including censored Gaussian data.G3 (Bethesda, Md.) · 2013Article
- Article
- Genome-wide prediction of discrete traits using Bayesian regressions and machine learning.Genetics, selection, evolution : GSE · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
The objective of this study was to detect interactions between relevant single-nucleotide polymorphisms (SNPs) associated with rheumatoid arthritis (RA). Data from Problem 1 of the Genetic Analysis Workshop 16 were used. These data consisted of 868 cases and 1,194 controls genotyped with the 500 k Illumina chip. First, machine learning methods were applied for preselecting SNPs. One hundred SNPs outside the HLA region and 1,500 SNPs in the HLA region were preselected using information-gain theory. The software weka was used to reduce colinearity and redundancy in the HLA region, resulting in a subset of 6 SNPs out of 1,500. In a second step, a parametric approach to account for interactions between SNPs in the HLA region, as well as HLA-nonHLA interactions was conducted using a Bayesian threshold least absolute shrinkage and selection operator (LASSO) model incorporating 2,560 covariates. This approach detected some main and interaction effects for SNPs in genes that have previously been associated with RA (e.g., rs2395175, rs660895, rs10484560, and rs2476601). Further, some other SNPs detected in this study may be considered in candidate gene studies.
Identifiers
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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.