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Lawrence Hunter (University of Colorado School of Medicine, Aurora, United States of America)
Genome scale assays (such as low-cost sequencing and microarrays) pose profound challenges in analyzing and explaining the resulting data. Most phenomena of interest to biomedical research involve the concerted activity of hundreds of genes and their products. Exploiting these results requires putting them in the context of relevant existing knowledge of biology. We have developed a knowledge-driven data analysis tool that brings information from gene-centric databases and the biomedical literature to bear on analysis and explanation of genome scale datasets [Leach, et al., 2009]. Based on semantic web technology, our tool creates an up-to-date, unified knowledge network useful in analyzing the results of nearly any genome-scale assay. The relevant knowledge can be explored in a visual analytic framework built on the Cytoscape network visualization tool. The alignment of data and knowledge networks facilitates development of explanations of the phenomena in the experimental data, straightforwardly directing analysts to particular database entries, journal articles and other sources of knowledge relevant to their analysis. Unlike any previous tool, these networks can also be used t