- Open Access
- Article
Biclustering Results Visualization of Gene Expression Data: A Review
by Haithem Aouabed 1 
, Mourad Elloumi 2 and Fahad Algarni 2
1 University of Sfax, Department of Computer Sciences, Faculty of Economic Sciences and Management, Sfax, 3018, Tunisia
2 University of Bisha, Department of Computer Sciences and artificial intelligence, College of Computing and Information Technology, Bisha, 67714, Saudi Arabia
* Author to whom correspondence should be addressed.
Journal of Engineering Research and Sciences, Volume 3, Issue 10, Page # 55-68, 2024; DOI: 10.55708/js0310006
Keywords: Biclustering algorithms, Biclusters, Overlaps, Visualization, Visualization techniques
Received: 26 August 2024, Revised: 09 October 2024, Accepted: 10 October 2024, Published Online: 29 November 2024
(This article belongs to the Special Issue Special Issue on Multidisciplinary Sciences and Advanced Technology 2024 & Section Biochemical Research Methods (BRM))
APA Style
Aouabed, H., Elloumi, M., & Algarni, F. (2024). Biclustering results visualization of gene expression data: A review. Journal of Engineering Research and Sciences, 3(10), 55-68. https://doi.org/10.55708/js0310006
Chicago/Turabian Style
Aouabed, Haithem, Mourad Elloumi, and Fahad Algarni. “Biclustering Results Visualization of Gene Expression Data: A Review.” Journal of Engineering Research and Sciences 3, no. 10 (2024): 55-68. https://doi.org/10.55708/js0310006.
IEEE Style
H. Aouabed, M. Elloumi, and F. Algarni, “Biclustering results visualization of gene expression data: A review,” Journal of Engineering Research and Sciences, vol. 3, no. 10, pp. 55-68, 2024, doi: 10.55708/js0310006.
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Abstract
Biclustering is a non-supervised data mining method used to analyze gene expression data by identifying groups of genes that exhibit similar patterns across specific groups of conditions. Discovering these co-expressed genes (called biclusters) can aid in understanding gene interactions in various biological contexts. Biclustering is characterized by its bi-dimensional nature, grouping both genes and conditions in the same bicluster and its overlapping property, allowing genes to belong to multiple biclusters. Biclustering algorithms often produce a large number of overlapping biclusters. Visualizing these results is not a straightforward task due to the specific characteristics of biclusters. In fact, biclustering results visualization is a crucial process to infer patterns from the expression data. In this paper, we explore the various techniques for visualizing multiple biclusters simultaneously and we evaluate them in order to help biologists to better choose their appropriate visualization techniques.
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