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Related Experiment Video

Updated: May 10, 2026

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

Module-based breast cancer classification.

Yuji Zhang¹, Jianhua Xuan, Robert Clarke

¹Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic College of Medicine, Rochester, Minnesota 55905, USA. Zhang.Yuji@mayo.edu

International Journal of Data Mining and Bioinformatics

|July 4, 2013

Summary

This summary is machine-generated.

This study introduces a new method for identifying cancer biomarkers as functional gene modules, improving accuracy and reproducibility in patient classification. These module biomarkers offer better biological insights and are linked to cancer drivers.

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Last Updated: May 10, 2026

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

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Published on: August 1, 2018

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Building Up a High-throughput Screening Platform to Assess the Heterogeneity of HER2 Gene Amplification in Breast Cancers

Published on: December 5, 2017

Area of Science:

Genomics
Bioinformatics
Cancer Research

Background:

Reliability and reproducibility of gene biomarkers for cancer patient classification are challenged by measurement noise and patient heterogeneity.
Existing methods often identify individual genes, which may not fully capture complex biological processes.

Purpose of the Study:

To develop and validate a novel module-based feature selection framework for identifying cancer biomarkers.
To integrate biological network information with gene expression data for more robust biomarker discovery.

Main Methods:

A module-based feature selection framework was proposed.
Biological network information and gene expression data were integrated.
The framework was applied to four independent breast cancer studies.

Main Results:

Module biomarkers achieved higher classification accuracy in independent validation datasets compared to individual gene markers.
Identified module biomarkers demonstrated improved reproducibility.
The results showed enhanced biological interpretability and enrichment in cancer disease drivers.

Conclusions:

Module-based biomarker identification offers a more reliable and reproducible approach for cancer classification.
This framework enhances biological understanding and identifies key cancer-driving functional modules.