Visualizing Biclustering Results on Gene Expression Data: A Survey

This is the authors' abstract. We don't add key points for this paper.

Biclustering is a non-supervised data mining technique used to analyze gene expression data, consists to classify subgroups of genes that behave similarly under subgroups of conditions. These same classified genes can behave independently under other subgroups of conditions. Discovering such co-expressed genes (called biclusters) can be helpful to find specific biological aims such as gene interactions under different circumstances. Biclustering has two characteristics: bi-dimensionality which means grouping both genes and conditions together 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 overlapped biclusters. Visualizing biclustering results still a non-trivial task due to biclustering features. In this paper, we make a survey on different techniques to visualize groups of biclusters in the same view.