Computational Biology and Bioinformatics

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Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique

Received: 24 September 2023    Accepted: 12 October 2023    Published: 30 October 2023
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Abstract

Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.

DOI 10.11648/j.cbb.20231102.11
Published in Computational Biology and Bioinformatics (Volume 11, Issue 2, December 2023)
Page(s) 19-32
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Biclustering Visualization, Two-Dimensional Matrix, Filtering, Overlaps, InfoVis

References
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Cite This Article
  • APA Style

    Haithem Aouabed, Mourad Elloumi. (2023). Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Computational Biology and Bioinformatics, 11(2), 19-32. https://doi.org/10.11648/j.cbb.20231102.11

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    ACS Style

    Haithem Aouabed; Mourad Elloumi. Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Comput. Biol. Bioinform. 2023, 11(2), 19-32. doi: 10.11648/j.cbb.20231102.11

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    AMA Style

    Haithem Aouabed, Mourad Elloumi. Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique. Comput Biol Bioinform. 2023;11(2):19-32. doi: 10.11648/j.cbb.20231102.11

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  • @article{10.11648/j.cbb.20231102.11,
      author = {Haithem Aouabed and Mourad Elloumi},
      title = {Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique},
      journal = {Computational Biology and Bioinformatics},
      volume = {11},
      number = {2},
      pages = {19-32},
      doi = {10.11648/j.cbb.20231102.11},
      url = {https://doi.org/10.11648/j.cbb.20231102.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.cbb.20231102.11},
      abstract = {Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.},
     year = {2023}
    }
    

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    T1  - Visualizing Biclusters of Gene Expression Data and Their Overlaps Based on a Two-Dimensional Matrix Technique
    AU  - Haithem Aouabed
    AU  - Mourad Elloumi
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    PY  - 2023
    N1  - https://doi.org/10.11648/j.cbb.20231102.11
    DO  - 10.11648/j.cbb.20231102.11
    T2  - Computational Biology and Bioinformatics
    JF  - Computational Biology and Bioinformatics
    JO  - Computational Biology and Bioinformatics
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    PB  - Science Publishing Group
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    UR  - https://doi.org/10.11648/j.cbb.20231102.11
    AB  - Biclustering is a data mining technique used to analyze gene expression data. It consists of classifying subgroups of genes that behave similarly under subgroups of conditions and can behave independently under other conditions. These discovered co-expressed genes (called biclusters) can help to find specific biological aims like finding characteristics of a specific disease. A large number of biclustering algorithms have been developed. Generally, these algorithms give as output a large number of overlapped biclusters. The visualization of these biclusters is still a non-trivial task. In this paper, we present a new approach to display biclustering results from gene expression data on the same screen. It is based on a two-dimensional matrix where each bicluster is represented as a column and each overlap between a set of biclusters is represented as a row. We illustrated the usefulness of our method with biclustering results from real and synthetic datasets and we compared it to other techniques that concentrate on biclustering overlaps issue. The method is implemented in a web-based interactive visualization tool called VisBicluster available at http://vis.usal.es/~visusal/visbicluster.
    VL  - 11
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    ER  - 

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Author Information
  • Computer Science Department, Faculty of Economic Sciences and Management, University of Sfax, Sfax, Tunisia

  • Computer Science Department, Faculty of Computing and Information Technology, University of Bisha, Bisha, Saudi Arabia

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