Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jun 26, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
09:40

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Glioma identification from microRNA biomarkers using machine learning.

Rakesh Kanth Andugala1, Alyson Cieslik2, Maria Braoudaki2

  • 1School of Physics, Engineering and Computer Science, University of Hertfordshire, Hatfield, United Kingdom.

Frontiers in Systems Biology
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Artificial Intelligence in Demand and Capacity Modelling of Healthcare Systems.

IEEE journal of biomedical and health informatics·2026
Same author

Editorial: Role of nanocarriers and non-coding RNAs in solid malignancies.

Frontiers in oncology·2025
Same author

New chalcone compound exhibits microrna-mediated anticancer properties in glioblastoma.

PloS one·2025
Same author

An Instrumental High-Frequency Smart Meter with Embedded Energy Disaggregation.

Sensors (Basel, Switzerland)·2025
Same author

Emerging roles of the cancerous inhibitor of protein phosphatase 2A (CIP2A) in ovarian cancer.

Scientific reports·2025
Same author

New microRNA-based therapies reveal common targets in paediatric medulloblastoma and adult glioblastoma.

Scientific reports·2025
Same journal

Computational analysis and validation of UGT1A1/4 missense variants impacting tecovirimat metabolism in monkeypox patients.

Frontiers in systems biology·2026
Same journal

Prioritizing long COVID related single nucleotide polymorphisms by mining genome-wide association studies of COVID-19 susceptibility and hospitalization.

Frontiers in systems biology·2026
Same journal

Multilayer network approaches to omics data integration in digital twins for cancer research.

Frontiers in systems biology·2026
Same journal

Quorum-quenching for bacterial pathogen control and health management in aquaculture: mechanisms, applications, current status and future prospects.

Frontiers in systems biology·2026
Same journal

Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.

Frontiers in systems biology·2026
Same journal

Duty of Care Governance: a conceptual framework for aligning health system scale with the clinician-patient relationship.

Frontiers in systems biology·2026
See all related articles

Machine learning accurately identifies gliomas using microRNA (miRNA) expression data. This approach aids in early detection and discovers novel miRNA biomarkers for brain tumor classification.

Area of Science:

  • Biomarkers and Diagnostics
  • Computational Biology and Bioinformatics
  • Oncology and Cancer Research

Background:

  • Gliomas are aggressive brain tumors, with traditional diagnostics being invasive and costly.
  • MicroRNAs (miRNAs) are key regulators of gene expression, and their dysregulation is implicated in cancer development.
  • miRNAs in bodily fluids offer a promising avenue for minimally invasive glioma detection and classification.

Purpose of the Study:

  • To develop and evaluate a machine learning (ML) pipeline for glioma identification using miRNA expression data.
  • To investigate potential miRNA biomarkers for distinguishing gliomas from controls and meningiomas.
  • To compare ML model performance using various feature selection and classification algorithms.

Main Methods:

Keywords:
MicroRNA biomarkersbrain cancergliomamachine learningmeningioma

More Related Videos

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

Related Experiment Videos

Last Updated: Jun 26, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy
09:40

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures
06:32

Evaluation of Biomarkers in Glioma by Immunohistochemistry on Paraffin-Embedded 3D Glioma Neurosphere Cultures

Published on: January 9, 2019

  • Utilized miRNA expression data from four Gene Expression Omnibus (GEO) datasets.
  • Applied five feature selection techniques (LASSO, mRMR, ReliefF, RFE, RF importance).
  • Employed six ML algorithms (LR, KNN, DT, RF, SVM, XGB) with and without SMOTE oversampling, assessed via 5-fold cross-validation.

    • Main Results:

    • Achieved up to 100% accuracy in binary classification (glioma vs. controls).
    • Reached up to 100% F1-score in multi-class classification (glioma vs. meningioma vs. controls) using KNN and XGB classifiers.
    • Identified seven potential miRNA biomarkers (miR-125a-3p, miR-4276, miR-4648, miR-4763-3p, miR-663a, miR-6784-5p, miR-873-3p), validated on an independent dataset.

    Conclusions:

    • The developed ML pipeline effectively classifies gliomas using miRNA expression data.
    • The identified miRNAs represent promising novel biomarkers for glioma diagnosis and classification.
    • This minimally invasive approach holds potential for improved early detection and personalized treatment of brain tumors.