abstract
- The study introduces a structured methodology for the identification of molecular targets that accurately classify medulloblastoma subgroups: WNT, SHH, Group 3 (G3) and Group 4 (G4). An artificial neural network (ANN) model trained on microarray gene expression data determined minimal gene combinations for each subgroup. The classification achieved an average accuracy of 96%, demonstrating the effectiveness of the proposed approach. Feature selection using the Kruskal¿Wallis and ¿2 tests revealed statistically relevant genes contributing to subgroup discrimination. Reverse transcription followed by digital Polymerase Chain Reaction (dPCR) measured the expression levels of a subset of these genes in tumor samples, validating the computational predictions with experimental evidence. The integration of machine learning and molecular quantification provides a reproducible framework for medulloblastoma subgroup classification supported by both statistical and experimental consistency. © 2025 The Authors