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. © Springer-Verlag Berlin Heidelberg 2007.
CITATION STYLE
Biehl, M., Breitling, R., & Li, Y. (2007). Analysis of tiling microarray data by learning vector quantization and relevance learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4881 LNCS, pp. 880–889). Springer Verlag. https://doi.org/10.1007/978-3-540-77226-2_88
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