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Research Article |

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

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.

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

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

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

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

Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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