Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling.

Journal: Clinical oncology (Royal College of Radiologists (Great Britain))

Published Date: Feb 11, 2025

Abstract

AIMS: Medulloblastoma (MB) is the most prevalent malignant brain tumour in children, characterised by substantial molecular heterogeneity across its subgroups. Accurate classification is pivotal for personalised treatment strategies and prognostic assessments. In this study, we aimed to build machine learning models to classify MB subgroups.

Authors

H Hourfar

Bioprocess Engineering Department, Institute of Industrial and Environmental Biotechnology, National Institute of Genetic Engineering and Biotechnology, Tehran, Iran.
P Taklifi

Department of Biotechnology, College of Sciences, University of Tehran, Tehran, Iran.
M Razavi

University Paris-Saclay, Paris, France.
B Khorsand

Department of Neurology, University of California, Irvine, CA, 92612, USA. Electronic address: Khorsand.babak@uci.edu.

Keywords

Cerebellar Neoplasms Child Child, Preschool Female Gene Expression Profiling Humans Machine Learning Male Medulloblastoma Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (40020441)

Machine Learning-Driven Identification of Molecular Subgroups in Medulloblastoma via Gene Expression Profiling.

Abstract

Authors

Keywords

External Resources

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