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Updated: Jun 19, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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

Published on: May 17, 2019

ArrayMining: a modular web-application for microarray analysis combining ensemble and consensus methods with

Enrico Glaab1, Jonathan M Garibaldi, Natalio Krasnogor

  • 1School of Computer Science, Nottingham University, Jubilee Campus, Wollaton Road, Nottingham, UK. enrico.glaab@cs.nott.ac.uk

BMC Bioinformatics
|October 30, 2009
PubMed
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ArrayMining.net simplifies DNA microarray analysis by integrating diverse algorithms and datasets using ensemble and consensus methods. This web application facilitates disease gene discovery through automated, combined analytical approaches.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • DNA microarray analysis is crucial for investigating genetic disease components.
  • Combining diverse algorithms and datasets enhances analytical power.
  • Current tools often lack integrated approaches for complex microarray data analysis.

Purpose of the Study:

  • To develop a unified web application for comprehensive DNA microarray analysis.
  • To enable the integration of multiple algorithms and cross-study data.
  • To simplify complex microarray analysis tasks through automation and modularity.

Main Methods:

  • Development of ArrayMining.net, a web application.
  • Implementation of ensemble learning, consensus clustering, and cross-study normalization.

Related Experiment Videos

Last Updated: Jun 19, 2026

Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
07:41

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

Published on: May 17, 2019

  • Integration of feature selection, clustering, prediction, and gene set analysis modules.
  • Automated parameter selection and linkage to annotation databases.

    • Main Results:

    • ArrayMining.net offers access to diverse analysis methods including ensemble feature selection and prediction.
    • The platform supports consensus clustering and cross-platform data integration.
    • Enables novel exploratory analyses, such as ensemble sample classification using integrated data.
    • Simplifies analysis through automatic parameter selection and database linkages.

    Conclusions:

    • ArrayMining.net is a free, web-based tool for advanced microarray analysis.
    • It leverages ensemble and consensus methods for robust data integration.
    • Features automatic parameter selection and seamless integration with annotation resources.