AI-derived prognostic model identifies high-risk gene signatures in pediatric gliomas
Our findings highlight the potential of AI-driven transcriptomic analysis in improving pediatric glioma prognosis. The identified gene signatures may serve as biomarkers for risk stratification and personalized treatment strategies, advancing precision oncology in pediatric neuro-oncology.
Authors
- Ying Li (ORCID: https://orcid.org/0000-0002-0882-5926)
- Weizhen Wang
- Fuyu Pei
Institutions
- Nanfang Hospital (CN)
- Guangdong Province Women and Children Hospital (CN)
- Southern Medical University (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2026-03-09
- DOI
- https://doi.org/10.3389/fimmu.2026.1704720
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 0.00