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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Updated: Jun 27, 2026

Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer
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Using Microarrays to Interrogate Microenvironmental Impact on Cellular Phenotypes in Cancer

Published on: May 21, 2019

Visualizing microarray data for biomarker discovery by matrix reordering and replicator dynamics.

Ying Liu1, Wenyuan Li

  • 1Department of Computer Science, University of Texas at Dallas, Richardson, TX, USA. ying.liu@utdallas.edu

Journal of Bioinformatics and Computational Biology
|December 18, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces a novel matrix reordering algorithm for analyzing microarray data. The method enhances visualization and improves sample classification accuracy by considering multiple sample classes.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray data analysis often involves multiple sample classes (e.g., normal vs. diseased).
  • Existing feature selection methods inadequately address multi-class patterns or prioritize differential gene expression.
  • There is a lack of effective visualization tools for biomarker discovery in microarray datasets.

Purpose of the Study:

  • To develop a novel visualization and analysis method for multi-class microarray data.
  • To improve biomarker discovery by effectively utilizing gene expression patterns across different sample classes.
  • To enhance the accuracy of sample classification using gene expression data.

Main Methods:

  • Generalization of replicator dynamics, a population genetic algorithm, for matrix reordering.
  • Application of matrix reordering to visualize and analyze multi-class microarray data.
  • Simultaneous consideration of global between-class and local within-class data patterns.

Main Results:

  • The proposed algorithm provides effective visualization for both genes and samples in multi-class microarray datasets.
  • The matrix reordering technique improves the accuracy of sample classification.
  • The method successfully integrates differential gene expression with multi-class data structures.

Conclusions:

  • Matrix reordering based on generalized replicator dynamics offers a powerful tool for microarray data analysis and visualization.
  • This approach enhances biomarker discovery by revealing complex gene-sample relationships.
  • The method improves classification performance, aiding in disease diagnosis and understanding.