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dc.contributor.authorAntonov, Alexey V.
dc.contributor.authorTetko, Igor V.
dc.contributor.authorKosykh, Denis
dc.contributor.authorSurmeli, Dmitrij
dc.contributor.authorMewes, Hans-Werner
dc.contributor.editorTorda, Andrew
dc.contributor.editorKurtz, Stefan
dc.contributor.editorRarey, Matthias
dc.date.accessioned2019-08-27T08:22:34Z
dc.date.available2019-08-27T08:22:34Z
dc.date.issued2005
dc.identifier.isbn3-88579-400-4
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/24922
dc.description.abstractIn 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.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofGerman Conference on Bioinformatics 2005 (GCB 2005)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-71
dc.titleExploiting scale-free information from expression data for cancer classificationen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages93-102
mci.conference.sessiontitleRegular Research Papers
mci.conference.locationHamburg
mci.conference.date5.-7. Oktober 2005


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