ArticleGenes and immunity2010
Supervised machine learning and logistic regression identifies novel epistatic risk factors with PTPN22 for rheumatoid arthritis.
Article in Genes and immunity, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 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.
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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
23 citing papers in PubMed, 45 citations in OpenAlex.
- Advances in Artificial Intelligence (AI) Models and Generative Algorithms Represent a New Paradigm for Genomics Research.International journal of molecular sciences · 2025Review
- The Veterans Affairs Rheumatoid Arthritis Registry: A unique population in rheumatoid arthritis research.Seminars in arthritis and rheumatism · 2025Review
- Artificial intelligence in autoimmune diseases: a bibliometric exploration of the past two decades.Frontiers in immunology · 2025Review
- Distributed transformer for high order epistasis detection in large-scale datasets.Scientific reports · 2024Article
- Article
- Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review.Rheumatology and therapy · 2022Review
- Improved diagnosis of rheumatoid arthritis using an artificial neural network.Scientific reports · 2022Article
- Big data analyses and individual health profiling in the arena of rheumatic and musculoskeletal diseases (RMDs).Therapeutic advances in musculoskeletal disease · 2022Review
- An introduction to machine learning and analysis of its use in rheumatic diseases.Nature reviews. Rheumatology · 2021Review
- The basics of data, big data, and machine learning in clinical practice.Clinical rheumatology · 2021Review
- Brief Survey on Machine Learning in Epistasis.Methods in molecular biology (Clifton, N.J.) · 2021Review
- Practical Implementation of Artificial Intelligence-Based Deep Learning and Cloud Computing on the Application of Traditional Medicine and Western Medicine in the Diagnosis and Treatment of Rheumatoid Arthritis.Frontiers in pharmacology · 2021Review
- Identification of predictive factors in hepatocellular carcinoma outcome: A longitudinal study.Oncology letters · 2020Article
- A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases.NPJ digital medicine · 2020Review
- Human Epistatic Interaction Controls IL7R Splicing and Increases Multiple Sclerosis Risk.Cell · 2017Article
- Network or regression-based methods for disease discrimination: a comparison study.BMC medical research methodology · 2016Article
- Insights and Implications of the VA Rheumatoid Arthritis Registry.Federal practitioner : for the health care professionals of the VA, DoD, and PHS · 2015Review
- LEAP: biomarker inference through learning and evaluating association patterns.Genetic epidemiology · 2015Article
- Lack of replication of interactions between polymorphisms in rheumatoid arthritis susceptibility: case-control study.Arthritis research & therapy · 2014Article
- Mining pure, strict epistatic interactions from high-dimensional datasets: ameliorating the curse of dimensionality.PloS one · 2012Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors at 7 institutions in 2 countries.
Funding
Abstract
Investigating genetic interactions (epistasis) has proven difficult despite the recent advances of both laboratory methods and statistical developments. With no 'best' statistical approach available, combining several analytical methods may be optimal for detecting epistatic interactions. Using a multi-stage analysis that incorporated supervised machine learning and methods of association testing, we investigated epistatic interactions with a well-established genetic factor (PTPN22 1858T) in a complex autoimmune disease (rheumatoid arthritis (RA)). Our analysis consisted of four principal stages: Stage I (data reduction)-identifying candidate chromosomal regions in 292 affected sibling pairs, by predicting PTPN22 concordance using multipoint identity-by-descent probabilities and a supervised machine learning algorithm (Random Forests); Stage II (extension analysis)-testing detailed genetic data within candidate chromosomal regions for epistasis with PTPN22 1858T in 677 cases and 750 controls using logistic regression; Stage III (replication analysis)-confirmation of epistatic interactions in 947 cases and 1756 controls; Stage IV (combined analysis)-a pooled analysis including all 1624 RA cases and 2506 control subjects for final estimates of effect size. A total of seven replicating epistatic interactions were identified. SNP variants within CDH13, MYO3A, CEP72 and near WFDC1 showed significant evidence for interaction with PTPN22, affecting susceptibility to RA.
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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.