Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDMW.2006.88
Thomas Wolf , TU Darmstadt, Germany
Benedikt Brors , Theoretical Bioinformatics DKFZ Heidelberg, Germany
Thomas Hofmann , 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) . 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 .
B. Brors, T. Hofmann, T. Wolf and E. Georgii, "Global Biclustering of Microarray Data," Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06)(ICDMW), Hong Kong, China, 2006, pp. 125-129.