Global Biclustering of Microarray Data

Wolf, T; Brors, B; Hofmann, T; Georgii, E

doi:10.1109/ICDMW.2006.88

Item

ITEM ACTIONSEXPORT

Add to Basket

Local TagsRelease HistoryDetailsSummary

Released

Conference Paper

Global Biclustering of Microarray Data

MPS-Authors

There are no MPG-Authors in the publication available

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4063611
(Publisher version)

Fulltext (restricted access)

There are currently no full texts shared for your IP range.

Fulltext (public)

There are no public fulltexts stored in PuRe

Supplementary Material (public)

There is no public supplementary material available

Citation

Wolf, T., Brors, B., Hofmann, T., & Georgii, E. (2006). Global Biclustering of Microarray Data. In S. Tsumoto, C. Clifton, N. Zhong, X. Wu, J. Liu, B. Wah, et al. (Eds.), Sixth IEEE International Conference on Data Mining - Workshops (ICDMW'06) (pp. 125-129). Los Alamitos, CA, USA: IEEE Computer Society.

Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-CF49-B

Abstract

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].