Exploiting scale-free information from expression data for cancer classification
ISSN der Zeitschrift
German Conference on Bioinformatics 2005 (GCB 2005)
Regular Research Papers
Gesellschaft für Informatik e.V.
In most studies concerning expression data analyses information on the variability of gene intensity across samples is usually exploited. This information is sensitive to initial data processing which affects the final conclusions. However expression data contains scale free information which is directly comparable between different samples. We propose to use the pairwise ratio of gene expression values rather than their absolute intensities for classification of expression data. This information is stable to data processing and thus more attractive for classification analyses. In proposed schema of data analyses only information on relative gene expression levels in each sample is exploited. Testing on publicly available datasets leads to superior classification results.