MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches.

ABSTRACT: Childhood medulloblastoma is a malignant form of brain tumor that is widely classified into four subgroups based on molecular and genetic characteristics. Accurate classification of these subgroups is crucial for appropriate treatment, monitoring plans, and targeted therapies. However, misclassification between groups 3 and 4 is common. To address this issue, an AI-based R package called MBMethPred was developed based on DNA methylation and gene expression profiles of 763 medulloblastoma samples to classify subgroups using machine learning and neural network models. The developed prediction models achieved a classification accuracy of over 96% for subgroup classification by using 399 CpGs as prediction biomarkers. We also assessed the prognostic relevance of prediction biomarkers using sur

SUBMITTER: Sharif Rahmani E 

PROVIDER: S-EPMC10513500 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature