Machine learning identification of molecular targets for medulloblastoma subgroups using microarray gene fingerprint analysis

Machine learning identification of molecular targets for medulloblastoma subgroups using microarray gene fingerprint analysis

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 combinati...

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Bibliographic Details
Main Authors: Alicia Reveles-Espinoza, Ulises Villela, Edgar Hernandez-Martinez, Isaac Chairez, Sergio Juárez-Méndez, J. Casanova-Moreno, Ma. del Pilar Eguía-Aguilar, Luis Figueroa-Yáñez, Adriana Vallejo-Cardona, Iván Salgado
Format: Article
Language:English
Published: Elsevier 2025-01-01
Series:Computational and Structural Biotechnology Journal
Subjects:
Medulloblastoma
Artificial neural networks
Gene microarray
Feature extraction
Online Access:http://www.sciencedirect.com/science/article/pii/S2001037025002995
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