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Título
A visual analytics approach for understanding biclustering results from microarray data
Autor(es)
Materia
Microarray analysis
Biclustering algorithms
Biclustering techniques
Técnicas de biclustering
Datos genómicos
Materia USAL
Bioinformática
Fecha de publicación
2008-05
Editor
BioMed Central (Londres, Gran Bretaña)
Citación
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
Resumen
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.
Descripción
Se presenta una técnica de biclustering para datos genómicos y una técnica de visualización de biclusters.
URI
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