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Global Biclustering of Microarray Data

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons83975

Brors B, Hofmann,  T
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

http://pubman.mpdl.mpg.de/cone/persons/resource/persons83929

Georgii,  E
Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Zitation

Wolf, T., Brors B, Hofmann, T., & Georgii, E. (2006). Global Biclustering of Microarray Data. Proceedings of the Sixth IEEE International Conference on Data Mining - Workshops (ICDMW 2006), 125-129.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0013-CF49-B
Zusammenfassung
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].