BioMed Central Genomics
Existing clustering approaches for microarray data do not adequately differentiate between subsets of co-expressed genes. We devised a novel approach that integrates expression and sequence data in order to generate functionally coherent and biologically meaningful subclusters of genes. Specifically, the approach clusters co-expressed genes on the basis of similar content and distributions of predicted statistically significant sequence motifs in their upstream regions.
Martyanov, Viktor and Gross, Robert H., "Identifying Functional Relationships within Sets of Co-Expressed Genes by Combining Upstream Regulatory Motif Analysis and Gene Expression Information" (2010). Open Dartmouth: Faculty Open Access Articles. 603.