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Julian Heinrich (University of Stuttgart, Stuttgart, Germany)In Parallel Coordinates, axes are placed in parallel and data points are represented by polylines crossing the axes. This allows for the visualization of an arbitrary number of variables and data points without using projections to lower dimensions. The patterns that emerge from a parallel coordinates display have proven to be a powerful tool in the knowledge discovery process for many different types of data, including geospatial, medical, financial, time-dependent or biological data. In addition, many extensions to parallel coordinates exist for nominal and continuous data, focus+context visualization, outlier preserving visualization, cluster visualization, integration of statistics and more. In combination with modern interaction techniques such as brushing & linking, parallel coordinates have become an invaluable tool in visual multidimensional data analysis. In this poster, we will give some examples on how to apply parallel coordinates on biological data with focus on gene expression data. Here, the parallel axes are mainly used to represent microarray experiments coming from time series or other comparative gene expression studies, while lines denote the expression of genes