ArticleBMC proceedings2007
Classification of rheumatoid arthritis status with candidate gene and genome-wide single-nucleotide polymorphisms using random forests.
Article in BMC proceedings, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
14 citing papers in PubMed, 34 citations in OpenAlex.
- Predicting dementia diagnosis from cognitive footprints in electronic health records: a case-control study protocol.BMJ open · 2020Article
- Pipeline design to identify key features and classify the chemotherapy response on lung cancer patients using large-scale genetic data.BMC systems biology · 2018Article
- Article
- Genome-wide association data classification and SNPs selection using two-stage quality-based Random Forests.BMC genomics · 2015Article
- An AUC-based permutation variable importance measure for random forests.BMC bioinformatics · 2013Article
- Correction for population stratification in random forest analysis.International journal of epidemiology · 2012Article
- Development of a predictive model for type 2 diabetes mellitus using genetic and clinical data.Osong public health and research perspectives · 2011Article
- Validation of psoriatic arthritis diagnoses in electronic medical records using natural language processing.Seminars in arthritis and rheumatism · 2011Article
- Random forests for genetic association studies.Statistical applications in genetics and molecular biology · 2011Review
- On safari to Random Jungle: a fast implementation of Random Forests for high-dimensional data.Bioinformatics (Oxford, England) · 2010Article
- An application of Random Forests to a genome-wide association dataset: methodological considerations & new findings.BMC genetics · 2010Article
- Bioinformatics challenges for genome-wide association studies.Bioinformatics (Oxford, England) · 2010Review
- Detecting significant single-nucleotide polymorphisms in a rheumatoid arthritis study using random forests.BMC proceedings · 2009Article
- Application of machine learning algorithms to predict coronary artery calcification with a sibship-based design.Genetic epidemiology · 2008Article
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Using the North American Rheumatoid Arthritis Consortium (NARAC) candidate gene and genome-wide single-nucleotide polymorphism (SNP) data sets, we applied regression methods and tree-based random forests to identify genetic associations with rheumatoid arthritis (RA) and to predict RA disease status. Several genes were consistently identified as weakly associated with RA without a significant interaction or combinatorial effect with other candidate genes. Using random forests, the tested candidate gene SNPs were not sufficient to predict RA patients and normal subjects with high accuracy. However, using the top 500 SNPs, ranked by the importance score, from the genome-wide linkage panel of 5742 SNPs, we were able to accurately predict RA patients and normal subjects with sensitivity of approximately 90% and specificity of approximately 80%, which was confirmed by five-fold cross-validation. However, in a complete training-testing framework, replication of genetic predictors was less satisfactory; thus, further evaluation of existing methodology and development of new methods are warranted.
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.