Abstract
An important goal in microarray data analysis is to identify sets of genes with similar expression profiles. Clustering algorithms have been applied on microarray data either to group the genes across experimental conditions/samples [396, 44, 302, 355, 448] or group the samples across the genes [317, 339, 400, 384]. Clustering techniques, which aim to find the clusters of genes over all experimental conditions, may fail to discover the genes having similar expression patterns over a subset of conditions. Similarly, a clustering algorithm that groups conditions/samples across all the genes may not capture the group of samples having similar expression values for a subset of genes.
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© 2011 Springer-Verlag Berlin Heidelberg
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Maulik, U., Bandyopadhyay, S., Mukhopadhyay, A. (2011). Multiobjective Biclustering in Microarray Gene Expression Data. In: Multiobjective Genetic Algorithms for Clustering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16615-0_10
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DOI: https://doi.org/10.1007/978-3-642-16615-0_10
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