Laboratory of Technologies of Information and Communication, and Electrical Engineering (LaTICE), University of Tunis, Tunis, Tunisia.
Faculty of Economic Sciences and Management of Sfax, University of Sfax, Sfax, Tunisia.
J Integr Bioinform. 2021 Oct 27;18(4):20210019. doi: 10.1515/jib-2021-0019.
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 , can be helpful to find specific biological features such as gene interactions under different circumstances. Compared to clustering, biclustering has two main characteristics: which means grouping both genes and conditions simultaneously and 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.
是一种非监督的数据挖掘技术，用于分析基因表达数据，它的目的是将具有相似行为的基因亚组分类，这些基因在其他条件的亚组下可能具有独立的行为。发现这样的共表达基因，称为 ，可以帮助在不同情况下找到特定的生物特征，如基因相互作用。与聚类相比，双聚类有两个主要特点：一是同时对基因和条件进行分组，二是允许基因同时存在于多个双聚类中。双聚类算法不断发展，输出大量重叠的双聚类。由于重叠，可视化重叠的双聚类仍然是一项艰巨的任务。在本文中，我们介绍了最有趣的可视化双聚类组的技术，并对其进行了评估。
