ArticleFrontiers in genetics2023
MBMethPred: a computational framework for the accurate classification of childhood medulloblastoma subgroups using data integration and AI-based approaches.
Article in Frontiers in genetics, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- EMMA-STRAT: a multi-omics based machine learning framework for stratification of endometrial carcinoma molecular subtypes and MSI status.BioData mining · 2026Article
- Deep learning-based prediction of gene expression from histopathology identifies NR5A1 as a candidate biomarker and druggable target in high-grade serous ovarian carcinoma.Journal of ovarian research · 2026Article
- SEMA4F Regulates the Malignant Phenotype of Hepatocellular Carcinoma Cells Through the PI3K-Akt Pathway.Journal of hepatocellular carcinoma · 2026Article
- Advancing Medulloblastoma Treatment: Molecular Mechanisms, Drug Repurposing, and Precision Therapies.Molecular diagnosis & therapy · 2026Review
- Serum-MiR-CanPred: deep learning framework for pan-cancer classification and miRNA-targeted drug discovery.RNA biology · 2025Article
- Integrative Machine Learning Framework for Enhanced Subgroup Classification in Medulloblastoma.Healthcare (Basel, Switzerland) · 2025Article
- OncoProExp: An interactive shiny web application for comprehensive cancer proteomics and phosphoproteomics analysis.Computational and structural biotechnology journal · 2025Article
- Tumor tissue-of-origin classification using miRNA-mRNA-lncRNA interaction networks and machine learning methods.Frontiers in bioinformatics · 2025Article
- Methods in DNA methylation array dataset analysis: A review.Computational and structural biotechnology journal · 2024Review
- Stanniocalcin Protein Expression in Female Reproductive Organs: Literature Review and Public Cancer Database Analysis.Endocrinology · 2024Review
- Review
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Authors and funding
6 authors.
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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 survival analysis. Furthermore, we identified subgroup-specific drivers of medulloblastoma using functional enrichment analysis, Shapley values, and gene network analysis. In particular, the genes involved in the nervous system development process have the potential to separate medulloblastoma subgroups with 99% accuracy. Notably, our analysis identified 16 genes that were specifically significant for subgroup classification, including
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