BIDEAL: A Toolbox for Bicluster Analysis -- Generation, Visualization and Validation
This provides a unified platform for researchers in bioinformatics and data analysis to apply biclustering methods, but it is incremental as it combines existing algorithms into a single toolbox.
The authors introduced BIDEAL, a toolbox that integrates multiple biclustering algorithms, visualization techniques, and validation indices to facilitate pattern extraction from data, such as for disease detection and biomarker identification, and demonstrated its effectiveness on biological datasets like Saccharomyces cerevisiae and Leukemia cancer.
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.