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An evaluation study of biclusters visualization techniques of gene expression data.

Haithem Aouabed1,2, Mourad Elloumi3, Rodrigo Santamaría4

  • 1Laboratory of Technologies of Information and Communication, and Electrical Engineering (LaTICE), University of Tunis, Tunis, Tunisia.

Journal of Integrative Bioinformatics
|October 26, 2021
PubMed
Summary

Biclustering, a data mining method, identifies gene expression patterns. This study reviews techniques for visualizing overlapping biclusters, aiding biological discovery.

Keywords:
biclustering algorithmsbiclustersinformation visualizationoverlapsvisualization

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Biclustering is a non-supervised data mining technique for gene expression analysis.
  • It identifies subgroups of genes with similar behavior across conditions, allowing for overlapping patterns.
  • Discovering biclusters aids in identifying gene interactions under various circumstances.

Purpose of the Study:

  • To present and evaluate visualization techniques for groups of biclusters.
  • To address the challenge of visualizing overlapping biclusters.

Main Methods:

  • Review of existing bicluster visualization techniques.
  • Evaluation of the effectiveness of different visualization methods for overlapping biclusters.

Main Results:

  • Identification of key visualization techniques for bicluster groups.
  • Assessment of the strengths and weaknesses of each technique in handling overlapping biclusters.

Conclusions:

  • Effective visualization of bicluster groups is crucial for biological interpretation.
  • The presented techniques offer valuable tools for researchers analyzing complex gene expression data.