In this paper we describe the use of a correlation clustering algorithm [Chaitanya, 2004] to group expression level of genes in a microarray dataset. The clustering problem is formalized as a semi-defined optimization program, based on the correlation provided by two quantities, respectively related to an agreement and a disagreement between a pair of genes. We also intend to validate the role of the correlation clustering algorithm by comparing the results with a support vectors clustering approach [Ben-Hur et al., 2001] that is demonstrated to perform well for many applications.
Pozzi, S., Zoppis, I., Mauri, G. (2005). Combinatorial and machine learning approaches in clustering microarray data. In Biological and Artificial Intelligence Environments (pp.63-71). Dordrecht : Springer [10.1007/1-4020-3432-6_8].
Combinatorial and machine learning approaches in clustering microarray data
ZOPPIS, ITALO FRANCESCO;MAURI, GIANCARLO
2005
Abstract
In this paper we describe the use of a correlation clustering algorithm [Chaitanya, 2004] to group expression level of genes in a microarray dataset. The clustering problem is formalized as a semi-defined optimization program, based on the correlation provided by two quantities, respectively related to an agreement and a disagreement between a pair of genes. We also intend to validate the role of the correlation clustering algorithm by comparing the results with a support vectors clustering approach [Ben-Hur et al., 2001] that is demonstrated to perform well for many applications.
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