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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this chapter
Cite this chapter
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
Download citation
DOI: https://doi.org/10.1007/978-3-642-16615-0_10
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16614-3
Online ISBN: 978-3-642-16615-0
eBook Packages: Computer ScienceComputer Science (R0)