Semi-supervised approach for finding cancer sub-classes on gene expression data

Authors:
Clerton Ribeiro;Francisco de Assis T. de Carvalho;Ivan G. Costa
Affiliations:
Center of Informatics, Federal University of Pernambuco, Recife, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, Brazil;Center of Informatics, Federal University of Pernambuco, Recife, Brazil
Venue:
BSB'10 Proceedings of the Advances in bioinformatics and computational biology, and 5th Brazilian conference on Bioinformatics
Year:
2010

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Abstract

The analysis of cancer gene expression is intrinsically a semi-supervised problem, as one is interested in building a classifier for diagnosis, but also on finding new sub-classes of cancer. We propose here a method for Mixture Discriminant Analysis (MDA), which can simultaneously detect sub-classes of cancer and perform classification. We evaluate the method on 10 gene expression data sets. MDA not only improved the classification in some of these data sets, as it detected some known and putative sub-classes of cancer.