Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

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

An Improved Deep Learning Algorithm for Breast Cancer Survival Prediction Based on Multi-Omics Data.

F1000Research·2026

Same author

Precision in prediction: tailoring machine learning models for breast cancer missense variants pathogenicity prediction.

Briefings in bioinformatics·2025

Same author

iKcr-DRC: prediction of lysine crotonylation sites in proteins based on a novel attention module and DenseNet.

Frontiers in genetics·2025

Same author

Comparative investigation of bagging enhanced machine learning for early detection of HCV infections using class imbalance technique with feature selection.

PloS one·2025

Same author

Metallothionein II treatment mitigates rotenone-induced neurodegeneration in zebrafish models of Parkinson's disease.

Frontiers in pharmacology·2025

Same author

AI-derived comparative assessment of the performance of pathogenicity prediction tools on missense variants of breast cancer genes.

Human genomics·2024

Search research articles

Related Experiment Video

Updated: Oct 18, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Enhanced Directed Random Walk for the Identification of Breast Cancer Prognostic Markers from Multiclass Expression

Hui Wen Nies¹, Mohd Saberi Mohamad², Zalmiyah Zakaria¹

¹School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia, Skudai 81310, Malaysia.

Entropy (Basel, Switzerland)

|September 28, 2021

Summary

This study introduces an enhanced directed random walk (eDRW+) method for identifying breast cancer prognostic markers. The eDRW+ method improves accuracy by considering different cancer subtypes and pathway importance, leading to better identification of potential drug targets.

Keywords:

ANOVA breast cancer directed random walk microarray analysis multiclass pathway selection prognostic markers

More Related Videos

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Published on: August 1, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Related Experiment Videos

Last Updated: Oct 18, 2025

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases

Published on: May 17, 2019

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Optimization of a Multiplex RNA-based Expression Assay Using Breast Cancer Archival Material

Published on: August 1, 2018

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Artificial intelligence (AI) shows promise in predicting disease probability in healthcare.
Breast cancer has five common molecular subtypes: luminal A, luminal B, basal, ERBB2, and normal-like.
Pathway-based microarray analysis aids in identifying prognostic markers from gene expression data.

Purpose of the Study:

To propose an enhanced directed random walk (eDRW+) algorithm for identifying breast cancer prognostic markers from multiclass expression data.
To address limitations of existing methods that ignore cancer subtype characteristics and treat all pathways equally.

Main Methods:

Developed eDRW+ incorporating an improved weighting strategy using one-way ANOVA (F-test).
Implemented pathway selection based on maximum reproducibility power.
Applied the method to multiclass breast cancer expression data.

Main Results:

The eDRW+ method demonstrated superior performance compared to existing methods, achieving higher Area Under the Curve (AUC) values.
Identified 294 gene markers and 45 pathway markers from breast cancer datasets with improved AUC.
The identified markers showed potential for distinguishing clinically distinct outcomes among cancer subtypes.

Conclusions:

The eDRW+ is an effective approach for identifying prognostic markers in multiclass breast cancer data.
The identified gene and pathway markers can aid in discovering drug targets and understanding clinically distinct cancer subtypes.