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2006
ACM

Two-phase clustering strategy for gene expression data sets

9 years 1 months ago
Two-phase clustering strategy for gene expression data sets
In the context of genome research, the method of gene expression analysis has been used for several years. Related microarray experiments are conducted all over the world, and consequently, a vast amount of microarray data sets are produced. Having access to this variety of repositories, researchers would like to incorporate this data in their analyses to increase the statistical significance of their results. In this paper, we present a new two-phase clustering strategy which is based on the combination of local clustering results to obtain a global clustering. The advantage of such a technique is that each microarray data set can be normalized and clustered separately. The set of different relevant local clustering results is then used to calculate the global clustering result. Furthermore, we present an approach based on technical as well as biological quality measures to determine weighting factors for quantifying the local results proportion within the global result. The better ...
Dirk Habich, Thomas Wächter, Wolfgang Lehner,
Added 14 Jun 2010
Updated 14 Jun 2010
Type Conference
Year 2006
Where SAC
Authors Dirk Habich, Thomas Wächter, Wolfgang Lehner, Christian Pilarsky
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