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Unsupervised meta-analysis on diverse gene expression datasets allows insight into gene function and regulation

Engelmann, Julia C., Schwarz, Roland, Blenk, Steffen, Friedrich, Torben, Seibel, Philipp N., Dandekar, Thomas and Müller, Tobias (2008) Unsupervised meta-analysis on diverse gene expression datasets allows insight into gene function and regulation. Bioinformatics and biology insights 2, pp. 265-280.

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Date of publication of this fulltext: 20 Aug 2014 08:26

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Abstract

Over the past years, microarray databases have increased rapidly in size. While they offer a wealth of data, it remains challenging to integrate data arising from different studies. Here we propose an unsupervised approach of a large-scale meta-analysis on Arabidopsis thaliana whole genome expression datasets to gain additional insights into the function and regulation of genes. Applying kernel ...

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Item type:Article
Date:May 2008
Institutions:UNSPECIFIED
Identification Number:
ValueType
19812781PubMed ID
Keywords:Arabidopsis thaliana; database; function prediction; gene expression; microarray; unsupervised meta-analysis
Dewey Decimal Classification:500 Science > 500 Natural sciences & mathematics
600 Technology > 610 Medical sciences Medicine
Status:Published
Refereed:Yes, this version has been refereed
Created at the University of Regensburg:No
Item ID:30679
Owner only: item control page

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