Rheumatoid Arthritis Diagnostic Genes Identified via Machine Learning

Three key genes—EPYC, MAGED1, and LAP3—were identified as potential diagnostic markers for rheumatoid arthritis using integrated bioinformatics and machine learning. The dataset, authored by Zhibin Zhang and last updated in April 2026, contains results from the analysis of public GEO transcriptomic datasets. It includes findings from feature selection, immune cell infiltration, and in vitro validation using TNF-α–stimulated HFLS-RA cells.

Use Cases

• Training diagnostic classifiers for rheumatoid arthritis based on the identified key genes EPYC, MAGED1, and LAP3.
• Analyzing immune cell infiltration patterns in synovial tissue based on associations with EPYC and LAP3.
• Conducting drug sensitivity analysis for rheumatoid arthritis based on exploratory use of CellMiner IC50 data.
• Validating gene expression modulation in inflammatory conditions based on TNF-α stimulation experiments.

Strengths

• Identifies three specific diagnostic genes (EPYC, MAGED1, LAP3) with reported AUC > 0.85.
• Results are derived from integrated bioinformatics, two machine learning algorithms (LASSO and SVM-RFE), and in vitro validation.
• Dataset is openly licensed under CC-BY-4.0.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• The dataset is very small (73.6 KB), indicating limited scope, likely containing summary results rather than raw expression data.

Provenance

Source

figshare

Collection Method

Analysis of public GEO transcriptomic datasets using bioinformatics and machine learning approaches.

Time Range

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Freshness

Last updated 2026-04-13 04:35:18; freshness should be verified.

Geography

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