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OBJECTIVE Although many cancer patients experience multiple concurrent symptoms, most studies have either focused on the analysis of single symptoms, or have used methods such as factor analysis that make a priori assumptions about how the data is structured. This article addresses both limitations by first visually exploring the data to identify patterns(More)
BACKGROUND In a recent study, two-dimensional (2D) network layouts were used to visualize and quantitatively analyze the relationship between chronic renal diseases and regulated genes. The results revealed complex relationships between disease type, gene specificity, and gene regulation type, which led to important insights about the underlying biological(More)
Although many cancer patients experience multiple concurrent symptoms, most studies have focused on the analysis of single symptoms. Furthermore, the few studies that have analyzed how symptoms co-occur across patients have used methods such as factor analysis that have a priori assumptions of how the data is structured. To address these limitations, we(More)
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