Exploiting scale-free information from expression data for cancer classification

Authors:
Alexey V. Antonov;Igor V. Tetko;Denis Kosykh;Dmitrij Surmeli;Hans-Werner Mewes
Affiliations:
GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstraíe 1, D-85764 Neuherberg, Germany;GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstraíe 1, D-85764 Neuherberg, Germany and Institute of Bioorganic and Petroleum Ch ...;GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstraíe 1, D-85764 Neuherberg, Germany;GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstraíe 1, D-85764 Neuherberg, Germany;GSF National Research Center for Environment and Health, Institute for Bioinformatics, Ingolstädter Landstraíe 1, D-85764 Neuherberg, Germany and Department of Genome-Oriented Bioinforma ...
Venue:
Computational Biology and Chemistry
Year:
2005

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Abstract

Most studies concerning expression data analyses usually exploit information on the variability of gene intensity across samples. 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 a 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.