Combining Pareto-optimal clusters using supervised learning for identifying co-expressed genes.

ABSTRACT:

Background

The landscape of biological and biomedical research is being changed rapidly with the invention of microarrays which enables simultaneous view on the transcription levels of a huge number of genes across different experimental conditions or time points. Using microarray data sets, clustering algorithms have been actively utilized in order to identify groups of co-expressed genes. This article poses the problem of fuzzy clustering in microarray data as a multiobjective optimization problem which simultaneously optimizes two internal fuzzy cluster validity indices to yield a set of Pareto-optimal clustering solutions. Each of these clustering solutions possesses some amount of information regarding the clustering structure of the input data. Motivated by this fact, a novel

SUBMITTER: Maulik U 

PROVIDER: S-EPMC2657792 | biostudies-literature | 2009 Jan

REPOSITORIES: biostudies-literature