Suitable Overlapping Set Visualization Techniques and Their Application to Visualize Biclustering Results on Gene Expression Data

Aouabed, Haithem; Santamaría, Rodrigo; Elloumi, Mourad

doi:10.1007/978-3-319-99133-7_16

Cited by 2 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Visualization issues, such as scalability and overlaps between biclusters, are ones of the most important open directions for researchers. A sophisticated combination between traditional visualization techniques, like heatmaps or parallel coordinates, and one of the novel visualization techniques, mentioned in the literature [ 33 ], can be a solution to visualize biclustering results.…”

Section: Discussionmentioning

confidence: 99%

“…Because heatmaps and parallel coordinates visualization techniques show their limits to draw high number of biclusters in the same view, especially with high rate of overlaps [ 31 ], some more sophisticated visualization techniques have been proposed based on a good combination between traditional gene expression visualization techniques, i.e., heatmaps and/or parallel coordinates, and sets visualization techniques [ 33 ], like Venn-like diagrams [ 31 ] and node-link diagrams [ 34 ].…”

Section: Biclustering Visualization Techniquesmentioning

confidence: 99%

See 1 more Smart Citation

An evaluation study of biclusters visualization techniques of gene expression data

Aouabed

Elloumi

Santamaría

2021

Journal of Integrative Bioinformatics

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Discussionmentioning

confidence: 99%