This paper introduces a novel toolbox named BIDEAL for the generation of biclusters, their analysis, visualization, and validation. The objective is to facilitate researchers to use forefront biclustering algorithms embedded on a single platform. A single toolbox comprising various biclustering algorithms play a vital role to extract meaningful patterns from the data for detecting diseases, biomarkers, gene-drug association, etc. BIDEAL consists of seventeen biclustering algorithms, three biclusters visualization techniques, and six validation indices. The toolbox can analyze several types of data, including biological data through a graphical user interface. It also facilitates data preprocessing techniques i.e., binarization, discretization, normalization, elimination of null and missing values. The effectiveness of the developed toolbox has been presented through testing and validations on Saccharomyces cerevisiae cell cycle, Leukemia cancer, Mammary tissue profile, and Ligand screen in B-cells datasets. The biclusters of these datasets have been generated using BIDEAL and evaluated in terms of coherency, differential co-expression ranking, and similarity measure. The visualization of generated biclusters has also been provided through a heat map and gene plot.

中文翻译：

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BIDEAL：Bicluster 分析工具箱——生成、可视化和验证

本文介绍了一个名为 BIDEAL 的新工具箱，用于生成双聚类、其分析、可视化和验证。目的是促进研究人员使用嵌入在单个平台上的前沿双聚类算法。包含各种双聚类算法的单一工具箱在从数据中提取有意义的模式以检测疾病、生物标志物、基因药物关联等方面发挥着至关重要的作用。 BIDEAL 由 17 种双聚类算法、三种双聚类可视化技术和六个验证指标组成。该工具箱可以通过图形用户界面分析多种类型的数据，包括生物数据。它还有助于数据预处理技术，即二值化、离散化、归一化、消除空值和缺失值。通过对酿酒酵母细胞周期、白血病癌症、乳腺组织特征和 B 细胞数据集中的配体筛选进行测试和验证，已经展示了开发工具箱的有效性。这些数据集的双簇是使用 BIDEAL 生成的，并根据一致性、差异共表达排名和相似性度量进行评估。还通过热图和基因图提供了生成的双簇的可视化。