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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 the attested quality of the local results, the stronger their impact on the global result.

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Metadaten
Author:Dirk Habich, Thomas Wächter, Wolfgang Lehner, Christian Pilarsky
URN:https://nbn-resolving.org/urn:nbn:de:gbv:hil2-opus-304
Document Type:Conference Proceeding
Language:English
Date of Publication (online):2011/04/18
Contributing Corporation:Technology Database Technology Group, University of Dresden
Release Date:2011/04/18
Source:KDML 2006: Knowledge Discovery, Data Mining, and Machine Learning 9.-13. Oktober 2006, Hildesheim
PPN:Link zum Katalog
Contributor:Althoff, Klaus-Dieter
Institutes:Fachbereich IV / Informatik
DDC classes:000 Allgemeines, Informatik, Informationswissenschaft / 000 Allgemeines, Wissenschaft / 004 Informatik
Licence (German):License LogoDeutsches Urheberrecht