Microarray data mining: A novel optimization-based approach to uncover biologically coherent structures

Background: DNA microarray technology allows for the measurement of genome-wide expression patterns. Within the resultant mass of data lies the problem of analyzing and presenting information on this genomic scale, and a first step towards the rapid and comprehensive interpretation of this data is gene clustering with respect to the expression patterns. Classifying genes into clusters can lead to interesting biological insights. In this study, we describe an iterative clustering approach to uncover biologically coherent structures from DNA microarray data based on a novel clustering algorithm EP_GOS_Clust. Results: We apply our proposed iterative algorithm to three sets of experimental DNA microarray data from experiments with the yeast Saccharomyces cerevisiae and show that the proposed iterative approach improves biological coherence. Comparison with other clustering techniques suggests that our iterative algorithm provides superior performance with regard to biological coherence. A...

• Type: Journal
• Year: 2008
• Where: BMCBI
• Authors: Meng Piao Tan, Erin N. Smith, James R. Broach, Christodoulos A. Floudas
• Keywords: Biological Coherence, BMCBI 2008, DNA Microarray, DNA Microarray Technology