Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease.

Journal: Pediatric rheumatology online journal

Published Date: Jun 2, 2025

Abstract

BACKGROUND: Kawasaki disease (KD), a pediatric systemic vasculitis, lacks reliable diagnostic biomarkers and exhibits immune heterogeneity, complicating clinical management. Current therapies face challenges in targeting specific immune pathways and predicting treatment responses.

Authors

Xia Wang

Department of Neurology, The Sixth People's Hospital of Huizhou City, Huizhou, China.
Lin Zhang

Laboratory of Molecular Translational Medicine, Centre for Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, Ministry of Education, Clinical Research Center for Birth Defects of Sichuan Province, West China Second Hospital, Sichuan University, Chengdu, Sichuan, 610041, China. Electronic address: zhanglin@scu.edu.cn.

Keywords

Biomarkers Child Child, Preschool Female Gene Expression Profiling Gene Regulatory Networks Humans Infant Inflammation Machine Learning Male Mucocutaneous Lymph Node Syndrome

External Resources

View on PubMed Access via DOI PubMed (40457357)

Integrative machine learning identifies robust inflammation-related diagnostic biomarkers and stratifies immune-heterogeneous subtypes in Kawasaki disease.

Abstract

Authors

Keywords

External Resources

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