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Biclustering algorithms for biological data analysis: a survey.

Sara C Madeira1, Arlindo L Oliveira

  • 1University of Beira Interior, Rua Marquês D'Avila e Bolama, Covilhã, Portugal. smadeira@di.ubi.pt

IEEE/ACM Transactions on Computational Biology and Bioinformatics
|October 20, 2006
PubMed
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Standard clustering methods struggle with gene expression data due to uncorrelated experimental conditions. Biclustering algorithms address this by simultaneously clustering genes and conditions to find subgroups with correlated activities.

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Data Mining

Background:

  • Standard clustering methods for gene expression data have limitations when gene activities are uncorrelated across experimental conditions.
  • Simultaneous clustering of genes and conditions is necessary to overcome these limitations.

Purpose of the Study:

  • To provide a comprehensive survey and classification of biclustering algorithms.
  • To analyze existing biclustering approaches based on bicluster types, patterns, search methods, evaluation strategies, and applications.

Main Methods:

  • Analysis of a large number of existing biclustering algorithms.
  • Classification of algorithms based on key characteristics.

Main Results:

Related Experiment Videos

  • Biclustering, also known as coclustering or direct clustering, identifies subgroups of genes and conditions with correlated activities.
  • The survey categorizes biclustering methods by the bicluster types and patterns they discover, search methodologies, evaluation techniques, and application domains.

    • Conclusions:

    • Biclustering offers a powerful approach to analyze complex gene expression data where standard methods fall short.
    • This survey provides a structured overview of the biclustering landscape, aiding researchers in selecting appropriate methods for their specific data analysis needs.