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An evaluation study of biclusters visualization techniques of gene expression data.
- Source :
-
Journal of integrative bioinformatics [J Integr Bioinform] 2021 Oct 27; Vol. 18 (4). Date of Electronic Publication: 2021 Oct 27. - Publication Year :
- 2021
-
Abstract
- Biclustering is a non-supervised data mining technique used to analyze gene expression data, it consists to classify subgroups of genes that have similar behavior under subgroups of conditions. The classified genes can have independent behavior under other subgroups of conditions. Discovering such co-expressed genes, called biclusters , can be helpful to find specific biological features such as gene interactions under different circumstances. Compared to clustering, biclustering has two main characteristics: bi-dimensionality which means grouping both genes and conditions simultaneously and overlapping which means allowing genes to be in more than one bicluster at the same time. Biclustering algorithms, which continue to be developed at a constant pace, give as output a large number of overlapping biclusters. Visualizing groups of biclusters is still a non-trivial task due to their overlapping. In this paper, we present the most interesting techniques to visualize groups of biclusters and evaluate them.<br /> (© 2021 Haithem Aouabed et al., published by De Gruyter, Berlin/Boston.)
- Subjects :
- Cluster Analysis
Gene Expression
Gene Expression Profiling
Algorithms
Data Mining
Subjects
Details
- Language :
- English
- ISSN :
- 1613-4516
- Volume :
- 18
- Issue :
- 4
- Database :
- MEDLINE
- Journal :
- Journal of integrative bioinformatics
- Publication Type :
- Academic Journal
- Accession number :
- 34699698
- Full Text :
- https://doi.org/10.1515/jib-2021-0019