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High-throughput technologies have established themselves as indispensable for the study of biological systems, from gene expression level changes, protein concentrations, to their modifications and interactions in complex diseases and systems. This kind of data analysis is not well served by the biostatistical techniques traditionally applied to biomedical and clinical data sets. Non-trivial patterns are most often discovered using visual and other computational tools applied to this data. We derive patterns and information from a series of differentially expressed genes on eight microarrays combined with analysis of promoter regions using regular expression-driven examination of short representative sequences called motifs. These non-trivial patterns are used to aid the discovery process through the use of information visualization and by harnessing userpsilas perceptual and cognitive capabilities.