Analysis of tiling microarray data by learning vector quantization and relevance learning

Authors:
Michael Biehl;Rainer Breitling;Yang Li
Affiliations:
Institute of Mathematics and Computing Science, University of Groningen, Groningen, The Netherlands;Groningen Bioinformatics Centre, University of Groningen, Haren, The Netherlands;Groningen Bioinformatics Centre, University of Groningen, Haren, The Netherlands
Venue:
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Year:
2007

Citing 4
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Abstract

We apply learning vector quantization to the analysis of tiling microarray data. As an example we consider the classification of C. elegans genomic probes as intronic or exonic. Training is based on the current annotation of the genome. Relevance learning techniques are used to weight and select features according to their importance for the classification. Among other findings, the analysis suggests that correlations between the perfect match intensity of a particular probe and its neighbors are highly relevant for successful exon identification.