Abstract
We propose a new method to deal with missing values in the gene expression data. It is applied to improve the quality of clustering genes with respect to their functionality. Calculations are run against real-life data, within the framework of self-organizing maps. The applied gene distances correspond to the rank-based Spearman correlation and entropy-based information measure.
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Grużdź, A., Ihnatowicz, A., Ślęzak, D. (2005). Gene Expression Clustering: Dealing with the Missing Values. In: Kłopotek, M.A., Wierzchoń, S.T., Trojanowski, K. (eds) Intelligent Information Processing and Web Mining. Advances in Soft Computing, vol 31. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-32392-9_63
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DOI: https://doi.org/10.1007/3-540-32392-9_63
Publisher Name: Springer, Berlin, Heidelberg
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