title: Beyond clustering of array expressions.
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.
authors.affiliation: Bioinformatics Research Lab, University of Louisiana at Lafayette, P.O. Box 44330, Lafayette, LA 70504, USA. logan@cacs.louisiana.edu
authors.forename: Raja
authors.initials: R
authors.lastname: Loganantharaj
mesh_terms: D016000:Cluster Analysis; D019295:Computational Biology / Q000379:methods; D020869:Gene Expression Profiling / Q000379:methods*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*