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Bioinformatics Vol. 17 no. 90001 2001
Pages S157-S164
© 2001 Oxford University Press

Feature selection for DNA methylation based cancer classification

Fabian Model , Péter Adorján , Alexander Olek and Christian Piepenbrock

Epigenomics AG, Kastanienallee 24, D-10435 Berlin, Germany

Received on February 6, 2001 ; revised on April 2, 2001 ; accepted on April 2, 2001

Molecular portraits, such as mRNA expression or DNA methylation patterns, have been shown to be strongly correlated with phenotypical parameters. These molecular patterns can be revealed routinely on a genomic scale. However, class prediction based on these patterns is an under-determined problem, due to the extreme high dimensionality of the data compared to the usually small number of available samples. This makes a reduction of the data dimensionality necessary. Here we demonstrate how phenotypic classes can be predicted by combining feature selection and discriminant analysis. By comparing several feature selection methods we show that the right dimension reduction strategy is of crucial importance for the classification performance. The techniques are demonstrated by methylation pattern based discrimination between acute lymphoblastic leukemia and acute myeloid leukemia.

Contact: Fabian.Model{at}epigenomics.com


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