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2013 IEEE 13th International Conference on Data Mining Workshops (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
ISBN: 0-7695-2702-7
pp: 125-129
Benedikt Brors , Theoretical Bioinformatics DKFZ Heidelberg, Germany
Thomas Hofmann , TU Darmstadt, Germany
Thomas Wolf , TU Darmstadt, Germany
Elisabeth Georgii , TU Darmstadt, Germany
We consider the problem of simultaneously clustering genes and conditions of a gene expression data matrix. A bicluster is defined as a subset of genes that show similar behavior within a subset of conditions. Finding biclusters can be useful for revealing groups of genes involved in the same molecular process as well as groups of conditions where this process takes place. Previous work either deals with local, bicluster-based criteria or assumes a very specific structure of the data matrix (e.g. checkerboard or block-diagonal) [11]. In contrast, our goal is to find a set of flexibly arranged biclusters which is optimal in regard to a global objective function. As this is a NP-hard combinatorial problem, we describe several techniques to obtain approximate solutions. We benchmarked our approach successfully on the Alizadeh B-cell lymphoma data set [1].
Benedikt Brors, Thomas Hofmann, Thomas Wolf, Elisabeth Georgii, "Global Biclustering of Microarray Data", 2013 IEEE 13th International Conference on Data Mining Workshops, vol. 00, no. , pp. 125-129, 2006, doi:10.1109/ICDMW.2006.88
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