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

URN to cite this document:: urn:nbn:de:bvb:355-epub-306795
DOI to cite this document:: 10.5283/epub.30679

Engelmann, Julia C. ; Schwarz, Roland ; Blenk, Steffen ; Friedrich, Torben ; Seibel, Philipp N. ; Dandekar, Thomas ; Müller, Tobias

Preview

License: Creative Commons: Attribution 3.0
PDF - Published Version
(503kB)

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

Alternative links to fulltext:Pubmed

Details

Preview

Export bibliographical data

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 ...