Work

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

Year: 2025

Type: article

Abstract: 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.

Source: Computational and Structural Biotechnology Journal

DOI: 10.1016/j.csbj.2025.07.033

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

Institutions: Instituto Politécnico Nacional, Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional, Tecnológico de Monterrey, Instituto Nacional de Pediatria, Center of Research and Technologic Development in Electrochemistry, Hospital Infantil de México Federico Gómez, Centro de Investigación y Asistencia en Tecnología y Diseño del Estado de Jalisco

Language: English

Topic: Gene expression and cancer classification

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,230

Funder: Secretaría de Investigación y Posgrado, Instituto Politécnico Nacional

Award: G3355847572

OpenAlex ID: W4412816153

FWCI: 0.253

Cites: 102

Cited by: 1

Related to: 10