Machine Learning–Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling
This study highlights the efficacy of ML algorithms in classifying MB subgroups using gene expression data. The integration of feature selection techniques substantially improves model performance, paving the way for enhanced personalised approaches in MB management.
Authors
- Hamdam Hourfar
- Mahsa Sadat Razavi (ORCID: https://orcid.org/0000-0001-7576-7052)
- Parisa Taklifi (ORCID: https://orcid.org/0000-0003-3650-6495)
- Behnoush Khorsand
Institutions
- University of California, Irvine (US)
- University of Tehran (IR)
- Université Paris-Saclay (FR)
- National Institute of Genetic Engineering and Biotechnology (IR)
Publication Details
- Journal
- Clinical Oncology
- Published
- 2025-02-11
- DOI
- https://doi.org/10.1016/j.clon.2025.103789
- Citations
- 8
- Primary Topic
- Glioma Diagnosis and Treatment
- Type
- article
- Field-Weighted Citation Impact
- 13.00