Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Journal: Frontiers in immunology

PMID: 38711508

Abstract

BACKGROUND: Rheumatoid arthritis (RA) is a systemic immune-related disease characterized by synovial inflammation and destruction of joint cartilage. The pathogenesis of RA remains unclear, and diagnostic markers with high sensitivity and specificity are needed urgently. This study aims to identify potential biomarkers in the synovium for diagnosing RA and to investigate their association with immune infiltration.

Authors

Ying-Kai Wu

Department of Orthopaedic, The Second Affiliated Hospital of Soochow University, Jiangsu, China.
Cai-De Liu

Department of General Practice, Affiliated Hospital of Weifang Medical University, Wei Fang, China.
Chao Liu

Anti-Drug Technology Center of Guangdong Province, National Anti-Drug Laboratory Guangdong Regional Center, Guangzhou 510230, China.
Jun Wu

Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
Zong-Gang Xie

Department of Orthopaedic, The Second Affiliated Hospital of Soochow University, Jiangsu, China.

Keywords

Arthritis, Rheumatoid Biomarkers Computational Biology Databases, Genetic Gene Expression Profiling Gene Regulatory Networks Humans Kinesins Machine Learning Ribonucleoside Diphosphate Reductase Support Vector Machine Synovial Membrane Transcriptome

External Resources

View on PubMed Access via DOI PubMed (38711508)

Machine learning and weighted gene co-expression network analysis identify a three-gene signature to diagnose rheumatoid arthritis.

Abstract

Authors

Keywords

External Resources

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