Discover Community Create You

Loading publication...

Noodle always counts identifier-free feature events for service operation. With your permission, Google Analytics can also estimate audience and engagement. Search text, publication identifiers, cookies, IP addresses and browser fingerprints are never included in Noodle's first-party event counters.

Privacy details

Article

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosus

1 Dec 2021

Abstract excerpt

BACKGROUND: Rheumatoid arthritis (RA) and systemic lupus erythematous (SLE) are autoimmune rheumatic diseases that share a complex genetic background and common clinical features. This study's purpose was to construct machine learning (ML) models for the genomic prediction of RA and SLE. METHODS: A total of 2,094 patients with RA and 2,190 patients with SLE were enrolled from the Taichung Veterans General...

Read the complete abstract on PubMed

Topics

Share this publication in a Topic to start or enrich a Post.

Share to Rheumatoid Arthritis Research and Therapies
Share to Systemic Lupus Erythematosus Research
Share to vaccines and immunoinformatics approaches

Methodology Data sources Editorial policy Privacy About Contact Helena Bioinformatics

Source

Identifiers

PMID: 34895289
DOI: 10.1186/s13040-021-00284-5

Provenance

PubMed metadata and Literature Corpus relationships. Corpus snapshot updated 7 Sept 2026.

Related research

Semantic proximity does not establish scientific evidence.

Research trail→

Click a neighbor to travelStep 1 · 12 closest

Machine learning approaches for the genomic prediction of rheumatoid arthritis and systemic lupus erythematosusPMID 34895289Read ↗

Machine learning approaches to identify systemic lupus erythematosus in anti-nuclear antibody-positive patients using genomic data and electronic health recordsSemantic proximity Read ↗

Random forests algorithm boosts genetic risk prediction of systemic lupus erythematosusSemantic proximity Read ↗

Machine learning methods applied to classify complex diseases using genomic dataSemantic proximity Read ↗

Machine learning-based prediction of rheumatoid arthritis with development of ACPA autoantibodies in the presence of non-HLA genes polymorphisms.Semantic proximity Read ↗

Application of machine learning algorithm for the prediction of lupus nephritis using SNP data, polygenic risk score, and electronic health record.Semantic proximity Read ↗

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysisSemantic proximity Read ↗

Exploration of biomarkers for systemic lupus erythematosus by machine-learning analysisSemantic proximity Read ↗

Polygenic Risk Score Predicts Earlier-Onset Adult Systemic Lupus Erythematosus and First-Year Renal Diseases in a Taiwanese Cohort.Semantic proximity Read ↗

The construction and validation of sub-phenotype-specific genetic risk scores in systemic lupus erythematosus: a novel approach using large-scale biobank dataSemantic proximity Read ↗

Prediction of New Risk Genes and Potential Drugs for Rheumatoid Arthritis from Multiomics Data.Semantic proximity Read ↗

Integrating genetic, epigenetic, and clinical signatures via machine learning for robust prediction of leflunomide response in rheumatoid arthritis: a multi-center validation study.Semantic proximity Read ↗

Robust SNP-based prediction of rheumatoid arthritis through machine-learning-optimized polygenic risk score.Semantic proximity Read ↗

⌁Select a neighboring publication to make it the new centre.