Abstract
This paper empirically compares six background correction methods aimed at removing unspecific background noise of the overall signal level measured by a scanner across microarrays. Using three published cDNA microarray datasets we investigated the effect of background correction on cancer classification in terms of the predictive performance of two classifiers (k-NN and support vector machine with linear kernel) induced from microarray data where a particular background correction method is applied, individually and in combination with a single-bias or double-bias-removal normalization method.
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Freitas, A., Castillo, G., Marcos, A.S. (2009). Effect of Background Correction on Cancer Classification with Gene Expression Data. In: Combi, C., Shahar, Y., Abu-Hanna, A. (eds) Artificial Intelligence in Medicine. AIME 2009. Lecture Notes in Computer Science(), vol 5651. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-02976-9_59
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DOI: https://doi.org/10.1007/978-3-642-02976-9_59
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-02975-2
Online ISBN: 978-3-642-02976-9
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