2026-09-14T13:19:24Zhttps://gredos.usal.es/oai/request

oai:gredos.usal.es:10366/217062025-06-05T12:40:28Zcom_10366_4386com_10366_4349com_10366_3946com_10366_3823col_10366_4387

00925njm 22002777a 4500 dc Therón Sánchez, Roberto author Santamaría Vicente, Rodrigo author Miguel Quintales, Luis Antonio author 2008-05 Microarray analysis is an important area of bioinformatics. In the last few years, biclustering has become one of the most popular methods for classifying data from microarrays. Although biclustering can be used in any kind of classification problem, nowadays it is mostly used for microarray data classification. A large number of biclustering algorithms have been developed over the years, however little effort has been devoted to the representation of the results. We present an interactive framework that helps to infer differences or similarities between biclustering results, to unravel trends and to highlight robust groupings of genes and conditions. These linked representations of biclusters can complement biological analysis and reduce the time spent by specialists on interpreting the results. Within the framework, besides other standard representations, a visualization technique is presented which is based on a force-directed graph where biclusters are represented as flexible overlapped groups of genes and conditions. This microarray analysis framework (BicOverlapper), is available at http://vis.usal.es/bicoverlapper. The main visualization technique, tested with different biclustering results on a real dataset, allows researchers to extract interesting features of the biclustering results, especially the highlighting of overlapping zones that usually represent robust groups of genes and/or conditions. The visual analytics methodology will permit biology experts to study biclustering results without inspecting an overwhelming number of biclusters individually. SATAMARÍA VICENTE, R., THERÓN SÁNCHEZ, R. y MIGUEL QUINTALES, L. A. (2008). A visual analytics approach for understanding biclustering results from microarray data. "BCM Bioinformatics", 9:247 http://hdl.handle.net/10366/21706 http://hdl.handle.net/10366/21706 Microarray analysis Biclustering algorithms Biclustering techniques Técnicas de biclustering Datos genómicos A visual analytics approach for understanding biclustering results from microarray data