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Updated: May 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Identification of potential biomarkers from microarray experiments using multiple criteria optimization.

Matilde L Sánchez-Peña¹, Clara E Isaza, Jaileene Pérez-Morales

¹Bio IE Lab, Industrial Engineering Department, University of Puerto Rico at Mayaguez, Mayagüez, Puerto Rico.

Cancer Medicine

|May 2, 2013

Summary

This summary is machine-generated.

Identifying cancer biomarker genes from microarray data is challenging due to variability and parameter adjustments. This study proposes a novel multiple criteria optimization (MCO) approach using data envelopment analysis (DEA) for repeatable and consistent biomarker discovery.

Keywords:

Cancer biomarkers cervical cancer data envelopment analysis microarray data analysis multiple criteria optimization

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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

Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray experiments generate large datasets for gene expression analysis.
Identifying consistent cancer biomarker genes across studies remains a significant challenge.
Existing methods require parameter adjustments, impacting reproducibility and comparability.

Purpose of the Study:

To develop a novel, parameter-free method for identifying potential cancer biomarker genes from microarray data.
To address the challenges of variability and incommensurability between different microarray experiments and platforms.
To propose a new framework for biomarker discovery that enhances repeatability and facilitates simultaneous analysis of diverse datasets.

Main Methods:

The study frames cancer biomarker identification as a multiple criteria optimization (MCO) problem.
Data envelopment analysis (DEA) is employed to find efficient solutions, identifying potential biomarker genes.
The proposed method eliminates the need for user-defined parameter adjustments and normalization procedures.

Main Results:

The data envelopment analysis (DEA) approach successfully identified potential cancer biomarker genes.
The method demonstrated repeatability and consistency across different microarray experiments and platforms.
Analysis of cervix cancer microarray databases yielded promising biomarker candidates.

Conclusions:

Modeling biomarker selection as a multiple criteria optimization (MCO) problem is feasible and effective.
Data envelopment analysis (DEA) offers a robust, parameter-free solution for identifying cancer biomarkers from microarray data.
This approach provides a new perspective, eliminating the need for arbitrary thresholds and complex normalization, thereby improving biomarker discovery accuracy and consistency.