Title: Beyond clustering of array expressions

Authors: Raja Loganantharaj

Addresses: Bioinformatics Research Lab, University of Louisiana at Lafayette, P.O. Box 44330, Lafayette, LA 70504, USA

Abstract: Microarray technology provides an opportunity to view transcriptions at genomic level under different experimental conditions. Generally, co-expressed genes, which are members of the same cluster, are expected to have similar function, but unfortunately it is not true due to various reasons including co-expression does not necessarily imply co-regulation. To improve the results of clustering, we investigate a method based on singular value decomposition (SVD) for integrating diverse data sources. We also introduce a new cluster evaluation method based on mutual information. Using time series data sets on yeast, we have empirically demonstrated the effectiveness of SVD as a data integrator.

Keywords: data integration; cluster validation; cluster evaluation; SVD; singular value decomposition; bioinformatics; clustering; array expressions; microarrays; time series data; yeast.

DOI: 10.1504/IJBRA.2009.026423

International Journal of Bioinformatics Research and Applications, 2009 Vol.5 No.3, pp.329 - 348

Published online: 11 Jun 2009 *

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